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Out",{"layout":8,"template":409,"featured":28,"author":410,"sourceCTA":411},"TheSourceArticle","michelle-gill","global-devsecops-report-2025",{"title":413,"description":414,"date":415,"timeToRead":416,"heroImage":417,"keyTakeaways":418,"articleBody":422},"Building AI teams that move fast and don’t burn out","Learn practical strategies for managing AI talent, accelerating decisions, and keeping top engineers engaged in fast-moving environments.","2025-12-09","5 min read","https://res.cloudinary.com/about-gitlab-com/image/upload/v1751463704/u3dshy4qn6rtrklfalx7.png",[419,420,421],"AI experts bring valuable depth but their strong opinions can create decision paralysis that slows down delivery timelines.","Four decision-making frameworks help teams move faster: assign single decision owners, separate planning from doing, require evidence for changes, and match communication to technical depth.","Retention requires meaningful challenges, transparent career paths, and dedicated time for learning in a rapidly evolving field.","Many leaders are focused on hiring top AI talent right now, but few are preparing for what happens next.\n\nWhen you set out to build an AI team, you’ll probably start by looking for people with natural curiosity, persistence, and broad technical skills spanning AI, machine learning, and software development. The right hires can work at the frontier, maintain deep knowledge, track emerging developments, and distinguish meaningful advances from marketing noise.\n\nBut assembling talented people and establishing a strong AI center of excellence is the straightforward part. The real test begins afterward: managing strong opinions, breaking decision deadlock, and keeping experts engaged when the field changes weekly.\n\n## The challenge of managing expertise\nIn my experience, the qualities that make AI engineers valuable also create management complexity. When you have 10 experts, you get 10 excellent approaches to each problem and 10 discussions requiring your input before anything launches. These are exactly the team members you need because they possess rare, specialized knowledge. They also hold firm perspectives, which can trigger extended debates and competing proposals where everyone has valid technical reasoning, but you still have to choose a single path forward.\n\nThis dynamic undermines speed. And in the age of AI, if something takes longer than two months from conception to production, it’s already stale. A large language model (LLM) may deliver similar capabilities first. Not every engineer can maintain this tempo, stay current with research while writing production code, and remain focused on goals when priorities frequently shift.\n\nYour role as a leader requires keeping the team advancing quickly, reaching conclusions that avoid infinite deliberation cycles, and assessing whether your current roster still fits the demands. Traditional engineering leadership approaches often fail in these conditions.\n\nHere's what produces results.\n\n## Four strategies for alignment and momentum\nBegin by simplifying your organizational hierarchy so extra management tiers don't extend decisions across multiple weeks. Compress your schedules to reflect innovation velocity and leverage deadline pressure to determine when to abandon failing experiments, when to develop talent, and when to provide an off-ramp. Establish clear, uncompromising expectations for adaptability and on-time delivery.\n\nAfter laying that groundwork, provide your specialists with these four strategies for making decisions and moving at the pace of AI:\n\n**Assign a single owner for every decision.** Once you've collected input, designate one person to make the final call. Set time limits for discussions with explicit criteria for success. Don't allow simultaneous debates across multiple communication channels.\n\n**Distinguish between planning and implementation.** After reaching a decision, commit to that direction for a defined timeframe. During this period, pause questions about the approach itself. Theoretical debates will continue endlessly if you allow them to. Choose a direction and collect real performance data before entertaining changes.\n\n**Require evidence, not just proposals, to change course.** Your success threshold doesn't need perfection; \"better than before\" can suffice. If a new method shows gains in your evaluation measures, give it serious consideration. If it doesn’t, move on quickly.\n\n**Communicate in your experts' language.** When working with people who reason through model architectures, embedding dimensions, and evaluation frameworks, don't force everything into business language. Business outcomes matter, but you're leading technical professionals solving technical challenges. Use technical vocabulary when appropriate, strategic language when it serves the goal, and understand which context requires which approach.\n\nThese strategies will help you ship faster and make better decisions. However, even with flawless implementation, you operate in an environment where rivals will launch breakthrough capabilities every few weeks and constantly try to recruit your strongest engineers. These strategies provide speed; keeping your talent is what keeps you in the game.\n\n## Maintaining momentum long-term\nAfter hiring strategically, implementing decision frameworks, and beginning delivery, you face the challenge of retention.\n\nHere’s what separates organizations that keep their AI talent from those that lose it:\n\n**Provide meaningful work.** The lack of a compelling vision, uninteresting challenges, and endless unresolved discussions destroy AI team motivation. Connect their contributions to a larger impact and reach decisions that enable actual progress.\n\n**Establish advancement opportunities.** AI positions have grown more complex faster than most organizational career structures have adapted. Define senior AI leadership in your company. Build transparent progression paths with milestones recognizing both technical mastery and strategic contribution. Leading AI professionals choose organizations offering visible growth potential, not just employment.\n\n**Support ongoing development.** The opportunity to tackle frontier problems, learn continuously, and stay ahead is why your team members joined the team in the first place. Protect space for this by enabling conference attendance, research time, and experimentation. This maintains your elite team's effectiveness in a field undergoing constant transformation.\n\nTechnology advances regardless of organizational readiness. Models will continue to improve, and competitors will continue to innovate. Success comes from engineers who deliver quickly without compromising quality, leaders who coordinate exceptional minds without limiting creativity, and teams that ship reliably despite operating in chaotic conditions. Support them well, and your organization will remain at the forefront of 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McCaslin",{"ai":387,"platform":395,"security":391},{"id":474,"title":5,"body":6,"category":6,"config":475,"content":476,"description":6,"extension":26,"meta":492,"navigation":28,"path":493,"seo":494,"slug":16,"stem":495,"testContent":6,"type":496,"__hash__":497},"pages/en-us/the-source/ai/index.yml",{"layout":8},[477,484],{"componentName":478,"type":478,"componentContent":479},"TheSourceCategoryHero",{"title":387,"description":480,"image":481},"Explore expert insights on how AI is transforming software development, and how organizations can get the most out of their AI investments.",{"config":482},{"src":483},"https://res.cloudinary.com/about-gitlab-com/image/upload/v1751463300/eoudcbj5aoucl0spsp0c.png",{"componentName":485,"type":485,"componentContent":486},"TheSourceCategoryMainSection",{"config":487},{"sourceCTAs":488},[489,490,491],"source-lp-how-to-get-started-using-ai-in-software-development","navigating-ai-maturity-in-devsecops","source-lp-ai-guide-for-enterprise-leaders-building-the-right-approach",{},"/en-us/the-source/ai",{"title":387,"description":480,"ogImage":483},"en-us/the-source/ai/index","category","wtQi5a4Yy8rZpv9pRFgz-LgiIdSY188tyR5WwsQyl-w",{"id":499,"title":5,"body":6,"category":6,"config":500,"content":501,"description":6,"extension":26,"meta":515,"navigation":28,"path":516,"seo":517,"slug":20,"stem":518,"testContent":6,"type":496,"__hash__":519},"pages/en-us/the-source/security/index.yml",{"layout":8},[502,508],{"componentName":478,"type":478,"componentContent":503},{"title":391,"description":504,"image":505},"Get up to speed on how organizations can ensure they're staying on top of evolving security threats and compliance requirements.",{"config":506},{"src":507},"https://res.cloudinary.com/about-gitlab-com/image/upload/v1751463273/aplkxrvwpii26xao5yhi.png",{"componentName":485,"type":485,"componentContent":509},{"config":510},{"sourceCTAs":511},[512,513,514],"source-lp-guide-to-dynamic-sboms","source-lp-devsecops-the-key-to-modern-security-resilience","application-security-in-the-digital-age",{},"/en-us/the-source/security",{"title":391,"description":504,"ogImage":507},"en-us/the-source/security/index","Yz-XSZ2w3Zg4r2_4aWlzq2kmfduukECmMNfXD6Ha26w",{"id":521,"title":5,"body":6,"category":6,"config":522,"content":523,"description":6,"extension":26,"meta":537,"navigation":28,"path":538,"seo":539,"slug":25,"stem":540,"testContent":6,"type":496,"__hash__":541},"pages/en-us/the-source/platform/index.yml",{"layout":8},[524,530],{"componentName":478,"type":478,"componentContent":525},{"title":395,"description":526,"image":527},"Learn how to build a DevSecOps framework that sets your team up for success, from planning to delivery.",{"config":528},{"src":529},"https://res.cloudinary.com/about-gitlab-com/image/upload/v1751463263/bdz7hmhpbmgwvoybcaud.png",{"componentName":485,"type":485,"componentContent":531},{"config":532},{"sourceCTAs":533},[534,535,536],"source-lp-the-ultimate-playbook-for-high-performing-devsecops-teams","source-lp-measuring-success-in-software-development-a-guide-for-leaders","source-lp-building-a-resilient-software-development-practice",{},"/en-us/the-source/platform",{"title":395,"description":526,"ogImage":529},"en-us/the-source/platform/index","u9v0Yrf14Lhx-hAKL_t8ViZ-OxgRjEc5QiV6CvI6bJc",[543,566,605],{"id":544,"title":545,"body":6,"category":16,"config":546,"content":548,"description":6,"extension":26,"meta":559,"navigation":28,"path":560,"seo":561,"slug":563,"stem":564,"type":430,"__hash__":565},"theSource/en-us/the-source/ai/the-next-wave-of-devsecops-team-of-one-manager-of-many.yml","The Next Wave Of Devsecops Team Of One Manager Of Many",{"layout":8,"template":409,"featured":28,"author":547,"sourceCTA":411},"lee-faus",{"title":549,"description":550,"date":551,"timeToRead":552,"heroImage":553,"keyTakeaways":554,"articleBody":558},"The next wave of DevSecOps: Team of one, manager of many","Understand why AI augmentation amplifies rather than replaces engineering talent when built on collaborative learning.","2025-11-18","6 min read","https://res.cloudinary.com/about-gitlab-com/image/upload/v1756302005/ntf0xsctetcx7uq1yfpy.png",[555,556,557],"DevSecOps collaboration creates knowledge mastery across domains, preparing engineers to effectively evaluate and apply AI tools in complex software delivery scenarios.","AI should augment human capability by handling routine tasks, not replace the cross-functional judgment that comes from deep collaborative learning and expertise.","Organizations that combine collaborative learning cultures with AI augmentation will outperform those viewing AI as a simple cost-reduction strategy.","AI is creating unprecedented leverage for individual engineers. Individual team members can now accomplish what once required entire teams. But here's the paradox everyone is missing: the engineers who will build these solo empires aren’t just expert coders. They've spent years in collaborative teams, absorbing knowledge across security, infrastructure, business logic, and quality assurance.\n\nThe software industry is racing toward a future of AI-augmented individual capability. Yet the foundation for this future is the very thing many organizations are abandoning: deep, cross-functional collaboration. Understanding this contradiction reveals the real role of AI in software delivery.\n\n## Collaboration as a foundation\nThe fundamental goal of DevSecOps is to establish a collaborative engineering culture that spans the entire software delivery lifecycle, from business strategy to technical implementation. This culture centers on reusability and best practices that directly improve developer productivity and delivery efficiency. Organizations achieve this through a dual-gate system: \n* **Human consensus-based code reviews** ensure knowledge transfer and maintain quality standards across disciplines.\n* **Automated quality and security gates** catch issues before they reach production.\n\nThis approach balances speed with control. It de-risks software change management while ensuring that acceleration doesn't come at the expense of stability or security.\n\nMost organizations stop here. They implement the processes, install the tooling, and measure the velocity improvements. But, they miss the deeper transformation happening beneath the surface.\n\n## The knowledge transfer engine\nThe collaborative model is fundamentally about learning and knowledge mastery at scale. Research in educational psychology, particularly [Bloom's Taxonomy of Learning](https://bokcenter.harvard.edu/taxonomies-learning), suggests that the highest form of mastery is achieved through teaching concepts to others. \n\nThis is where the dual-gate system reveals its deeper value. Code reviews become structured knowledge transfer sessions. Each person operates as the knowledge expert in their domain while learning from adjacent domains: \n* The security engineer reviewing code teaches secure development practices while learning about business requirements\n* The architect understands product priorities while sharing knowledge about technical constraints\n* The junior developer learns patterns from seniors while bringing fresh perspectives on tooling\n\nThis creates a network effect where each person's knowledge elevates everyone else's capabilities. Expertise flows in all directions across the organization. This collaborative culture fosters a learning organization in which every interaction creates opportunities for teaching and accelerated growth.\n\nWhen you view DevSecOps through this lens, code review becomes a teaching moment. Security scans are a learning opportunity. Every interaction in the system enables knowledge transfer and mastery development. This is what sets certain engineers apart: They’ve internalized knowledge from adjacent domains through years of collaborative interaction.\n\n\n## The team of one: AI as a peer, not a replacement\nThe natural evolution of this collaborative model is the \"team of one,\" a knowledge worker augmented by AI that enables unprecedented autonomy and efficiency. The promise is compelling. Every engineer gains AI peers that handle lower-level work, such as remembering, understanding, and basic application of concepts. Teaching an agent to perform these redundant tasks dramatically lowers cognitive load, freeing mental capacity for higher-order thinking, including analysis, evaluation, and creative problem-solving. \n\nThis is how AI can amplify human capabilities rather than replace them. [Recent GitLab research](https://about.gitlab.com/developer-survey/) found that although 83% of DevSecOps professionals feel that AI will significantly change their role within the next five years, 76% agree that AI will actually create the need for more engineers, not fewer.\n\nHowever, a dangerous counter-narrative is emerging in executive circles. Some leaders believe highly capable AI agents can replace knowledge workers entirely. This represents a fundamental misunderstanding of how people develop expertise. \n\nEven with highly capable AI, you still need human experts who can:\n* Evaluate outputs across multiple disciplines\n* Establish trust in AI recommendations\n* Provide domain-specific judgment\n* Take accountability for production systems\n\nIn fact, GitLab’s research found that 40% of DevSecOps professionals agree that Al will actually accelerate career growth for junior developers.\n\nThe argument that \"we don't need junior developers anymore\" ignores the fact that someone still needs to review, validate, and take accountability for what AI produces. Junior developers aren't just writing code — they’re learning to evaluate it across multiple domains, building the judgment needed to verify AI outputs.\n\nThe opposite argument — that AI might replace experienced architects and senior developers — is equally problematic. This logic suggests we could skip foundational learning entirely and restructure computer science education to focus only on prompting AI agents. But without understanding what good code looks like across security, infrastructure, and business domains, how would these graduates know whether AI outputs are correct? Both extremes miss the point.\n\n## The real constraint: Scarcity of collective wisdom\nThe real constraint isn't AI capability. It's the scarcity of people who can actually operate as that \"team of one.\" You need engineers with sufficient skills across multiple domains to effectively evaluate AI outputs in security, infrastructure, quality, and business logic. And you need educators who understand how to develop these multi-skilled practitioners. \n\nThe collaborative model from the original DevSecOps goal remains essential because this is the mechanism through which people develop that breadth of knowledge. The team of one isn't someone working in isolation. It's someone who has internalized the collective wisdom of the cross-functional team and can now operate with AI augmentation while maintaining the judgment and accountability that only human expertise provides.\n\n## The path forward\nOrganizations face a critical choice. The tempting path is to view AI as a cost-reduction strategy by replacing expensive senior talent with cheaper tools and whoever can operate them. This path leads to brittle systems, technical debt, and ultimately failure. \n\nThe sustainable path recognizes that AI is a tool that amplifies existing capability but cannot replace the judgment that comes from deep, cross-functional mastery.\n\nThe companies that will win are those that double down on collaborative learning while simultaneously investing in AI augmentation. They understand that creating a team of one requires first creating a team that teaches each individual across multiple domains. They recognize that the code review process helps to transfer the knowledge needed to use AI tools effectively. They invest in building knowledge-transfer systems that create engineers capable of operating autonomously, having learned from the collective.\n\nThis is the paradox of the AI age in software delivery. As our AI tools become increasingly capable, the value of collaborative learning becomes even more pronounced. The only way to create people capable of effectively wielding those tools is through the cross-functional knowledge transfer enabled by DevSecOps. \n\nThe goal hasn't changed. We still need to improve productivity, increase efficiency, and reduce risk. What's changed is our understanding that achieving those goals at scale requires both collaborative learning and AI augmentation, not a choice between them.\n\nThe future belongs to organizations that build cultures where everyone teaches, everyone learns, and everyone becomes capable of operating as a team of one when augmented by AI. Ultimately, the real competitive advantage isn't AI; it's the people who know how to effectively apply it.",{},"/en-us/the-source/ai/the-next-wave-of-devsecops-team-of-one-manager-of-many",{"config":562,"title":549,"description":550},{"noIndex":427},"the-next-wave-of-devsecops-team-of-one-manager-of-many","en-us/the-source/ai/the-next-wave-of-devsecops-team-of-one-manager-of-many","uB108kAT7FF5Vzz0yhqNLa2CiJ961jVzF7UcnptOwEA",{"id":567,"title":568,"body":6,"category":25,"config":569,"content":572,"description":6,"extension":26,"meta":598,"navigation":28,"path":599,"seo":600,"slug":602,"stem":603,"type":430,"__hash__":604},"theSource/en-us/the-source/platform/code-is-currency-in-the-software-defined-finance-era.yml","Code Is Currency In The Software Defined Finance Era",{"layout":8,"template":409,"featured":28,"author":570,"sourceCTA":571},"joshua-carroll","software-innovation-report-2025",{"title":573,"description":574,"date":575,"timeToRead":416,"heroImage":576,"keyTakeaways":577,"articleBody":581,"faq":582},"Code is currency in the software-defined finance era","Speed drives success in today's code-driven economy. Here’s why financial organizations must adopt DevSecOps practices to compete. ","2025-11-13","https://res.cloudinary.com/about-gitlab-com/image/upload/v1751464105/b0sltyrnkzt8yzgikqgl.jpg",[578,579,580],"Finance is now software at its core — every transaction, payment, and compliance check runs on code, making technology the actual business product, not just infrastructure.","Development velocity determines competitive advantage — organizations that deploy daily and learn instantly outpace those still planning quarterly and executing monthly.","Security and compliance must be built into deployment pipelines, not added later — when policies become code, they scale with growth and accelerate rather than block progress.","Here’s what changed while everyone was debating digital transformation: money became code.\n\nNot metaphorically. Literally. The payment you just received? Software executed it. The loan decision that took seconds? Algorithms, not underwriters. The compliance check that happened invisibly in the background? Code reviewing code.\n\nThe shift happened gradually, then suddenly. And most organizations are still operating like it’s 2015. They treat technology as infrastructure when, in fact, it is the product. They manage risk in quarterly reviews while threats evolve hourly. They ship updates monthly while markets move in milliseconds.\n\n## The new physics of financial services\n\nSoftware follows its own laws. It compounds. It scales non-linearly. It fails in cascades. More importantly, it evolves at the speed of deployment, not the speed of planning.\n\nWhen your business runs on software, these become business fundamentals. A deployment pipeline determines how fast you can respond to markets. A security vulnerability becomes an existential threat that compounds every hour it exists. A compliance framework either enables or blocks transactions.\n\nThe organizations thriving in this reality have absorbed one truth: in software-defined finance, your release cycle is your innovation cycle. You don’t plan quarterly and execute monthly. You ship daily and learn instantly.\n\n## When speed and safety collide\n\nHere’s the paradox that breaks most financial organizations: the faster you need to move, the more trust you must maintain. And trust in finance takes years to build but only seconds to destroy.\n\nTraditional thinking says slow down, add checkpoints, and review everything twice. But that’s industrial-age logic applied to information-age problems. You can’t inspect quality into software any more than you can inspect trust into a financial system.\n\nThe answer is to make security a property of speed itself. When every commit triggers scans, every deployment generates audit logs, and every configuration change is instantly reversible, security becomes an accelerator rather than a brake.\n\nUltimately, this is about architecture, not tools. When security policies are code, they evolve with your product. When compliance is automated, it scales with your growth. When systems assume breach and contain blast radius by design, you can move faster because you’ve reduced the cost of being wrong.\n\n## Why integration can't wait\n\nMost organizations still operate in silos because that’s how they’ve always operated. But software doesn’t respect organizational boundaries. A vulnerability in development becomes a breach in production. A compliance gap in one service can cascade across the enterprise. A single API slowdown can stall entire payment flows.\n\nThe reflexive response is to buy more tools and build more bridges between them. But that is just digitizing dysfunction. When your developers, security, ops, and compliance teams each live in different toolchains, you haven't solved the silo problem — you've automated it. Every handoff becomes an API. Every integration becomes technical debt. Every tool boundary is where context dies.\n\nThe organizations winning this game collapse these boundaries with unified workflows:\n\n* The same commit that adds a feature also updates its security profile\n* The same pipeline that deploys code also generates compliance evidence\n* The same dashboard that shows system health also shows security posture\n* The same team that owns a feature owns its reliability, security, and compliance\n\nWhen developers see the security implications of their code in real time, they write more secure code. When security policies are code that flows through the same pipeline as features, protection scales automatically. When compliance evidence generates from actual development activity rather than manual attestations, trust becomes provable.\n\nThe leaders aren't just integrating their tools better. They're rethinking the entire architecture of how software-defined financial services are built, secured, and shipped. They understand that in a world where finance runs on software, your platform architecture is your business architecture.\n\n## The great divergence\n\nIt's not the tech stack. It's not the talent. It's not even the funding.\n\nThe best fintechs don't have better developers. They've figured out the secret everyone's missing: in software-defined finance, your process and tools determine how fast you can move.\n\nFragment your workflow across fifteen platforms? You'll move like fifteen companies trying to coordinate. Unify everything into one flow? You'll move like software actually moves: continuously, securely, inevitably forward.\n\nThe leaders of these organizations aren't trying harder. They're playing a different game entirely. New regulation? That's tomorrow's update. Security patch? That ships with the next feature. Compliance audit? Here's the dashboard.\n\nWhen friction disappears, everything accelerates. When everything accelerates, you stop competing on the old metrics. You're not winning the game anymore — you're playing a different one.\n\n## What actually matters\n\nDigital transformation and disruption are yesterday’s conversations.\n\nToday’s question is simpler: How fast can you ship secure, compliant, trusted code?\n\nThe finance industry has become inseparable from technology, which is essentially technology that facilitates the movement of money. Your advantage isn’t your charter, your partnerships, or your marketing. It’s your ability to turn ideas into running code before markets shift. It’s your deployment frequency, your security automation, your compliance as code.\n\nThe infrastructure exists. The patterns are proven. The only question is whether you’ll adapt to the new physics of finance, or insist on the old rules while the world accelerates past you.",[583,586,589,592,595],{"header":584,"content":585},"How has finance become software-defined and why does this matter?","Finance has become software at its core where every transaction, payment, and compliance check runs on code rather than manual processes. Money became code literally, not metaphorically - software executes payments, algorithms make loan decisions in seconds, and code reviews code for compliance. This makes technology the actual business product, not just supporting infrastructure.",{"header":587,"content":588},"Why do organizations that deploy daily have competitive advantages over those deploying monthly?","In software-defined finance, release cycles determine innovation cycles. Organizations shipping daily and learning instantly can respond to markets in real-time, while those planning quarterly and executing monthly operate at industrial-age speeds in information-age markets. Deployment pipelines determine how fast organizations can respond to market changes.",{"header":590,"content":591},"How should financial organizations approach security in fast-moving development environments?","Security must become a property of speed itself rather than a brake on velocity. When every commit triggers scans, every deployment generates audit logs, and every configuration change is instantly reversible, security becomes an accelerator. When security policies are code evolving with products and compliance is automated scaling with growth, organizations can move faster.",{"header":593,"content":594},"What is the cost of fragmented toolchains in software-defined finance?","Fragmenting workflows across fifteen platforms makes organizations move like fifteen companies trying to coordinate. Every handoff becomes an API, every integration becomes technical debt, and every tool boundary is where context dies. This digitizes dysfunction rather than solving silo problems, as developers, security, ops, and compliance teams in different toolchains automate isolation.",{"header":596,"content":597},"What defines successful organizations in software-defined finance?","Successful organizations unify workflows where the same commit updates security profiles, the same pipeline deploys code and generates compliance evidence, and the same dashboard shows system health and security posture. Leaders turn ideas into running code before markets shift through deployment frequency, security automation, and compliance as code rather than competing on traditional metrics.",{},"/en-us/the-source/platform/code-is-currency-in-the-software-defined-finance-era",{"config":601,"title":573,"description":574},{"noIndex":427},"code-is-currency-in-the-software-defined-finance-era","en-us/the-source/platform/code-is-currency-in-the-software-defined-finance-era","05MIgcy8ddI6nmfpUcym5HWEtMUnaB1rvRIopwRyDN4",{"id":606,"title":607,"body":6,"category":25,"config":608,"content":610,"description":6,"extension":26,"meta":637,"navigation":28,"path":638,"seo":639,"slug":641,"stem":642,"type":430,"__hash__":643},"theSource/en-us/the-source/platform/unlocking-software-driven-business-transformation-in-telco.yml","Unlocking Software Driven Business Transformation In Telco",{"layout":8,"template":409,"featured":427,"author":609,"sourceCTA":534},"marco-caronna",{"title":611,"description":612,"date":613,"timeToRead":614,"heroImage":615,"keyTakeaways":616,"articleBody":620,"faq":621},"Unlocking software-driven business transformation in telco","How AI-native DevSecOps and GitOps help telcos open new revenue streams, accelerate innovation, and outflank tech-native competitors.","2025-11-04","7 min read","https://res.cloudinary.com/about-gitlab-com/image/upload/v1762195291/e2zdeclyrqtso3flxlyw.png",[617,618,619],"Telcos must transform from hardware-centric infrastructure to software-driven operations to compete with digital-native companies capturing value from their networks.","A unified AI-native platform with DevSecOps and GitOps eliminates operational silos, accelerates innovation through agentic AI, and automates security and compliance.","GitOps and infrastructure automation enable 5G network function deployments with version-controlled configurations, audit trails, and instant rollback capabilities.","Over the past several years, I've watched telecommunications companies navigate an increasingly difficult paradox. The telco executives I speak with routinely describe investing billions in network infrastructure, only to struggle capturing value from those investments. The numbers tell a sobering story: in 2024, the return on invested capital (ROIC) for telcos dropped well below the median weighted average cost of capital (WACC), [falling as far as 6.7%](https://www.bcg.com/publications/2025/boosting-value-creation-in-telcos).\n\n\nWhat makes this particularly frustrating for these leaders is watching  digital-native companies like Netflix, WhatsApp, and Google generate [massive revenues](https://wjarr.com/sites/default/files/WJARR-2024-0113.pdf) using the very infrastructure telcos built. Time and again, I've seen telcos relegated to the sidelines as their networks, which are increasingly commoditized, create immense value for tech companies and hyperscalers.\n\n\nThe telecommunications executives who grasp this reality understand that Communication Service Providers (CSPs) and Network Equipment Providers (NEPs) must transform into software-driven technology companies. In my experience working with industry leaders, those that make the \"telco-to-techco\" transition position themselves for competitive advantage and unlock new growth opportunities that their competitors miss.\n\n## Why legacy software development approaches undermine telco innovation\nWhether you are a CSP diversifying into digital services or a NEP delivering cloud-native network functions, you need a modern software development foundation on which to build. Unfortunately, legacy processes prevent many telcos from establishing this foundation.\n\n### Innovation velocity gaps\nSome telcos have release cycles that are 18+ months long. Digital-native competitors, on the other hand, iterate through continuous deployment cycles in days or weeks. Hardware-based deployments play a big role in creating these innovation bottlenecks, prompting the industry shift toward software-based alternatives.\nThe innovation velocity gap impacts CSPs' revenue diversification efforts because new digital services require rapid iteration to find market fit. Subsidiaries tasked with entering IoT, edge computing, or digital payment solutions struggle to compete when constrained by legacy processes.\n\nNEPs face similar pressures, as lengthy development and deployment cycles strain relationships with CSPs who need faster deployment and reconfiguration of network modernization solutions.\n\n### Security and compliance overhead\nRegulatory requirements add complexity, leading to more inefficiency. The General Data Protection Regulation (GDPR) requires comprehensive data lineage tracking and audit trails. Industry best practices, including ETSI guidance on NFV testing and operations (such as NFV-TST 006), recommend that CSPs and NEPs adopt synchronized software delivery processes through continuous integration and delivery (CI/CD).\n\nUnfortunately, due to fragmented development toolchains, telcos must aggregate data across incompatible systems, maintain audit trails, and coordinate deployments — often in a manual fashion.\n\n### Barriers to AI-powered automation\nAI-powered software development presents a major opportunity for telcos, but fragmented toolchains hinder effective AI implementations. This may partly explain why [telcos trail other industries](https://about.gitlab.com/the-source/platform/whats-next-in-devsecops-for-telecommunications/) in operationalizing AI.\n\nWhen contextual data is sprawled across disparate systems, it leaves AI with a limited view of your business. Because AI systems thrive on context, this restricted view leads to poor outputs.\n\nSecurity and governance further complicate matters. Telcos must carefully control which data AI tools can access, implement governance policies, and avoid vendors that train their models on proprietary data.\n\nLastly, popular AI point solutions fixate on code generation while neglecting the broader software development lifecycle. Developers spend [less than a quarter of their time writing code](https://about.gitlab.com/developer-survey/). What about the other three-quarters of the development process? Telcos that rely on these AI coding assistants will struggle to meaningfully improve their time-to-market.\n\n## Driving telco transformation with a unified AI-native platform\nA unified AI-native platform that enables both DevSecOps and GitOps practices removes the many barriers to telco transformation. It accelerates innovation on a large scale while strengthening security and compliance.\n\n### Platform unification removes operational silos\nA unified platform eliminates operational silos that cause widespread inefficiency. It replaces fragmented toolchains with integrated workflows, enabling better collaboration between network operations and digital services teams — and removes the need to translate data between incompatible systems. Key outcomes include:\n* **Greater capital efficiency**: CSPs avoid excessive tool investments across subsidiaries and business units\n* **Accelerated market responsiveness**: Teams launch new services faster through better coordination\n* **Vendor ecosystem optimization**: NEPs deliver software updates and network functions through standardized processes that CSPs can integrate seamlessly\n* **Strategic execution alignment**: Network modernization and revenue diversification initiatives improve with shared visibility and better coordination\n* **Reduced talent waste**: Engineering resources focus on customer value creation rather than managing tools\n\n### Agentic AI acts as a force multiplier\nWhen a platform natively integrates agentic AI capabilities across both development and infrastructure operations, it yields large-scale productivity gains. The platform orchestrates autonomous AI workflows across the entire software lifecycle, enabling human-AI collaboration with complete business context. This delivers:\n* **Revenue acceleration through automation**: Addresses the entire software development process beyond code generation, enabling telcos to launch digital services faster and capture emerging IoT, edge computing, 5G applications, and other high-growth digital markets\n* **Dramatic cycle time reduction**: Development cycles accelerate from months to weeks/days\n* **Operational cost reduction**: Automatic CI/CD pipeline failure diagnosis and deployment optimization reduce engineering overhead, freeing teams for innovation\n* **Data sovereignty and privacy protection**: Privacy-first AI architecture with self-managed deployment options for air-gapped environments, granular permissions control, and commitment to never training models on customer data\n* **Improved competitiveness**: Enables telcos to dramatically increase their innovation velocity while maintaining regulatory compliance\n\n### Automated security and compliance removes transformation barriers\nThe platform’s DevSecOps capabilities eliminate the trade-off between speed and compliance. They automate security scanning and compliance workflows throughout the development lifecycle, strengthening telcos’ security posture while accelerating time-to-market. Critical business outcomes include:\n* **Cyber risk reduction**: Automated threat detection and vulnerability management protect against cyber attacks\n* **Supply chain transparency**: Complete visibility into third-party software components and dependencies reduces vendor risk\n* **Compliance workflow automation**: GDPR data protection, CSRD environmental reporting, and ETSI/BEREC standards adherence through automated workflows eliminates manual compliance overhead\n* **Audit readiness**: Real-time traceability and documentation reduce the time and cost of regulatory audits while ensuring continuous compliance\n* **Operational resilience**: Proactive security monitoring and automated policy enforcement prevent security incidents that can cause costly service disruptions\n\n### Infrastructure automation enables network modernization\nThe platform’s GitOps capabilities address the manual infrastructure deployments and configuration management challenges that slow telco transformation. The platform treats all network infrastructure as version-controlled code, enabling automated, consistent deployments across complex environments. Infrastructure automation delivers:\n* **Network function modernization**: Automates 5G and cloud-native network function deployments using Kubernetes orchestration, reducing deployment time while ensuring consistency across environments\n* **Multi-vendor coordination**: Standardized deployment processes enable synchronized delivery between NEPs and CSPs, meeting ETSI requirements while accelerating time-to-market for new services\n* **Risk mitigation through automation**: Version-controlled infrastructure configurations provide complete audit trails and instant rollback capabilities, reducing the operational risk that regulatory bodies and boards scrutinize\n* **Operational cost reduction**: Reduces manual configuration management, freeing engineers to focus on strategic initiatives\n\n> Learn how [Deutsche Telekom](https://about.gitlab.com/customers/deutsche-telekom/) achieved dramatic business results with GitLab, demonstrating the competitive advantage of a unified platform for telcos.\n\n## Your transformation opportunity\nThreats from digital-native competitors continue to mount. By transforming into software-driven technology companies, telcos can overcome these threats and capture more value from their infrastructure investments.\n\nA unified AI-native platform that enables both DevSecOps and GitOps practices removes transformation barriers. It eliminates operational silos, accelerates innovation through agentic AI, automates security and compliance, and enables the infrastructure automation critical for network modernization and 5G monetization.\n\nTelcos that act decisively today may emerge as industry leaders tomorrow.",[622,625,628,631,634],{"header":623,"content":624},"Why do telcos need to transition from hardware-centric to software-driven approaches?","Telcos invest billions in network infrastructure but struggle to capture value from those investments. Return on invested capital dropped well below the median weighted average cost of capital, falling as far as 6.7%. Digital-native companies like Netflix, WhatsApp, and Google generate massive revenues using telco infrastructure. Telcos watch from the sidelines as their increasingly commoditized networks create immense value for tech companies and hyperscalers.",{"header":626,"content":627},"What legacy software development challenges undermine telco innovation?","Legacy challenges include innovation velocity gaps with release cycles of 18+ months. Digital-native competitors iterate through continuous deployment cycles in days or weeks. Security and compliance overhead requires manual data aggregation across fragmented incompatible systems. Barriers to AI-powered automation occur when fragmented toolchains prevent effective AI implementations, leaving agents with limited business context.",{"header":629,"content":630},"How do unified AI-native platforms accelerate telco transformation?","Unified platforms eliminate operational silos by replacing fragmented toolchains with integrated workflows. This enables better collaboration between network operations and digital services teams. The platforms remove the need to translate data between incompatible systems. They orchestrate autonomous AI workflows across the entire software lifecycle with complete business context. Development cycles accelerate from months to weeks or days.",{"header":632,"content":633},"What business outcomes do DevSecOps capabilities provide for telecommunications companies?","DevSecOps capabilities deliver cyber risk reduction through automated threat detection and vulnerability management. They provide supply chain transparency with complete visibility into third-party components. Compliance workflow automation covers GDPR, CSRD, and ETSI/BEREC standards, eliminating manual overhead. Audit readiness comes through real-time traceability, reducing regulatory audit time and cost. Operational resilience improves through proactive security monitoring and automated policy enforcement.",{"header":635,"content":636},"How does GitOps enable network modernization for telecommunications companies?","GitOps treats all network infrastructure as version-controlled code. This enables automated consistent deployments across complex environments. It automates 5G and cloud-native network function deployments using Kubernetes orchestration. Standardized deployment processes enable multi-vendor coordination meeting ETSI requirements. Version-controlled configurations provide complete audit trails and instant rollback capabilities. This reduces manual configuration management, freeing engineers for strategic initiatives.",{},"/en-us/the-source/platform/unlocking-software-driven-business-transformation-in-telco",{"config":640,"title":611,"description":612},{"noIndex":427},"unlocking-software-driven-business-transformation-in-telco","en-us/the-source/platform/unlocking-software-driven-business-transformation-in-telco","vaTlm6Z0vp3x0E7NjRpMnZ58lHbxB3hTNG0U0nLyQrY",[645,652,680],{"id":406,"title":407,"body":6,"category":16,"config":646,"content":647,"description":6,"extension":26,"meta":649,"navigation":28,"path":424,"seo":650,"slug":428,"stem":429,"type":430,"__hash__":431},{"layout":8,"template":409,"featured":28,"author":410,"sourceCTA":411},{"title":413,"description":414,"date":415,"timeToRead":416,"heroImage":417,"keyTakeaways":648,"articleBody":422},[419,420,421],{},{"config":651,"title":413,"description":414},{"noIndex":427},{"id":653,"title":654,"body":6,"category":16,"config":655,"content":661,"description":6,"extension":26,"meta":671,"navigation":28,"path":672,"seo":673,"slug":676,"stem":677,"type":678,"__hash__":679},"theSource/en-us/the-source/ai/webcast-nov18-dora-gitlab-maximizing-ai-impact.yml","Webcast Nov18 Dora Gitlab Maximizing Ai Impact",{"layout":8,"template":409,"featured":427,"speakers":656,"gatedAsset":660},[657,658,659],"nathen-harvey","emilio-salvador","jessie-young","dora-insights-2025",{"title":662,"description":663,"date":664,"keyTakeaways":665,"articleBody":669,"heroImage":670},"AI as an amplifier: DORA and GitLab on maximizing AI impact","Watch this webinar with DORA and GitLab experts to discover what really determines AI success in software development teams.","2025-11-18T16:00Z",[666,667,668],"Organizational capabilities, not tools, determine AI success. High-performing organizations see accelerated value delivery while fragmented teams experience magnified dysfunction.","Research shows a direct correlation between high-quality internal platforms and an organization's ability to unlock AI value at scale.","While executives project $750B in potential value and expect 50/50 human-AI partnerships, current reality shows humans doing 75% of the work. Understanding this gap is key to effective AI adoption.","***Is AI helping or hurting your software development team?***\n\nNew research from GitLab reveals that AI-powered software innovation is the new economic growth engine, potentially unlocking billions in value.\n\nAt the same time, DORA research reveals a critical insight: AI doesn't create high performance. It amplifies what already exists. In well-aligned organizations, AI accelerates value delivery and improves flow. In fragmented ones, it exposes bottlenecks and magnifies dysfunction.\n\nThe implications are profound. Organizations are pouring resources into AI tools, expecting transformative results. Yet many are discovering that without the right foundation — quality internal platforms, clear workflows, aligned teams — AI investments fall short. Meanwhile, teams with strong organizational capabilities are seeing exponential gains.\n\nNathen Harvey from DORA and GitLab's Emilio Salvador and Jessie Young came together for a fireside chat exploring what really determines whether AI investments pay off.\n\n## What you'll learn\n\n**The current state of AI adoption:** Get the latest numbers from GitLab's annual research on how organizations are adopting AI and where C-level executives and DevSecOps practitioners have rising concerns.\n\n**The AI amplifier effect:** Discover why organizational capabilities, not AI tools, determine whether AI helps or hurts your software delivery performance. Learn how AI acts as both a mirror and multiplier, reflecting and amplifying your organization's existing strengths and weaknesses. Understand why the greatest returns on AI investment come from strategic focus on your underlying organizational system rather than the tools themselves.\n\n**The flexibility imperative:** Learn why successful AI adoption requires the flexibility to choose and switch between different AI tools and models rather than being locked into a single vendor solution. Understand how providing teams with options for AI model selection builds trust by allowing them to use tools that best fit their specific use cases, compliance requirements, and comfort levels.\n\n**The seven capabilities that matter:** Learn about the DORA AI Capabilities Model and understand which organizational factors unlock AI's potential and which expose existing weaknesses. Gain a practical framework for assessing where your organization stands and identify the specific capabilities you need to develop to maximize AI impact on your team.\n\n**Platform engineering's critical role:** See the direct correlation between high-quality internal platforms and an organization's ability to capture AI value. Discover why platform quality, workflow clarity, and team alignment are the true differentiators in AI success, and learn how to build platforms that enable experimentation while providing necessary standardization.\n\n**The human-AI partnership paradox:** While 73% of executives believe AI-human partnership should be at least 50/50, the current reality is that humans handle three quarters of the work. Learn what this disconnect means for your team and how to bridge it effectively, while preserving the human contributions that matter most: creativity and strategic vision.\n\n**From AI hype to AI value:** Move beyond adoption metrics to implementation insights. Learn how practitioners are securing executive buy-in using AI insights, navigating the tension between standardization and experimentation, and addressing the reality that technology can only help you as much as you can help yourself. Discover actionable strategies that you can turn into real outcomes for your organization.\n\n## Who should watch\n\nThis webinar is designed for:\n\n* Engineering leaders evaluating AI investment strategies\n* Platform engineering teams building internal developer platforms\n* Technical decision-makers responsible for tooling and productivity\n* DevSecOps practitioners implementing AI-assisted workflows\n* Software development managers looking to optimize team performance\n\nWatch the on-demand webinar to get exclusive access to the latest DORA and GitLab research reports and hear expert commentary on the findings.\n\nWhether you're just beginning your AI journey or scaling existing implementations, you'll gain actionable insights on aligning organizational capabilities to maximize AI impact.","https://res.cloudinary.com/about-gitlab-com/image/upload/v1760556275/zvygc1uasccpzg3sdvo6.png",{},"/en-us/the-source/ai/webcast-nov18-dora-gitlab-maximizing-ai-impact",{"config":674,"title":675,"description":663,"ogImage":670},{"noIndex":427},"DORA and GitLab on maximizing AI impact on software delivery","webcast-nov18-dora-gitlab-maximizing-ai-impact","en-us/the-source/ai/webcast-nov18-dora-gitlab-maximizing-ai-impact","webinar","E5dvJXA9gVhoipFEwoviUuiXpV9DpOkA8m6EOtaiTpE",{"id":544,"title":545,"body":6,"category":16,"config":681,"content":682,"description":6,"extension":26,"meta":684,"navigation":28,"path":560,"seo":685,"slug":563,"stem":564,"type":430,"__hash__":565},{"layout":8,"template":409,"featured":28,"author":547,"sourceCTA":411},{"title":549,"description":550,"date":551,"timeToRead":552,"heroImage":553,"keyTakeaways":683,"articleBody":558},[555,556,557],{},{"config":686,"title":549,"description":550},{"noIndex":427},[688,726,765],{"id":689,"title":690,"body":6,"category":20,"config":691,"content":693,"description":6,"extension":26,"meta":719,"navigation":28,"path":720,"seo":721,"slug":723,"stem":724,"type":430,"__hash__":725},"theSource/en-us/the-source/security/ai-agents-are-reshaping-software-what-cisos-need-to-know.yml","Ai Agents Are Reshaping Software What Cisos Need To Know",{"layout":8,"template":409,"featured":28,"author":692,"sourceCTA":571},"josh-lemos",{"title":694,"description":695,"date":696,"timeToRead":416,"heroImage":697,"keyTakeaways":698,"articleBody":702,"faq":703},"AI agents are reshaping software: What CISOs need to know","Most executives believe AI agents will dominate software development by 2028. Here’s what security leaders must do to prepare today.","2025-10-21","https://res.cloudinary.com/about-gitlab-com/image/upload/v1761059283/rolzub9bctnigdo573kb.png",[699,700,701],"Nearly 9 in 10 executives expect AI agents to become standard in software development within three years, creating urgent security challenges.","Organizations lack proper AI governance, with nearly half missing regulatory compliance and internal policies for artificial intelligence systems.","Security leaders can prepare by implementing identity policies, monitoring frameworks, and upskilling teams for the AI-driven software future.","New research from GitLab shows that 89% of C-level executives surveyed expect AI agents will become the standard approach for building software within three years. This transformation brings significant security implications, as 85% of these leaders recognize that AI agents will introduce never-before-seen security challenges.\n\nThe findings highlight a critical dilemma facing CISOs and security professionals: They can’t afford to pause AI adoption, but they must address the emerging risks it creates. With 91% of executives surveyed planning to boost their AI investments in software development over the next 18 months, each new AI breakthrough intensifies these security concerns.\n\n## AI governance gaps create adoption barriers\nSecurity leaders clearly understand the primary risks associated with [AI agents](https://about.gitlab.com/the-source/ai/agentic-ai-unlocking-developer-potential-at-scale/). Survey participants identified cybersecurity threats (52%), data privacy and security concerns (51%), and governance challenges (45%) as their top worries. These interconnected risks continue to evolve as the technology advances.\n\nOrganizations need robust AI governance frameworks to adapt their security approaches in response to emerging threats. However, this is easier said than done, since AI impacts multiple technology areas, from data governance to identity and access management. GitLab’s research indicates that organizations are falling behind in governance frameworks as many surveyed leaders said their organizations haven’t implemented regulatory-aligned governance (47%) or internal policies (48%) around AI.\n\nThis governance gap is the result of legitimate industry-wide challenges that make it difficult for leaders to focus their efforts effectively. AI agents behave unpredictably due to their non-deterministic nature, which disrupts traditional security boundaries. Additionally, new universal protocols such as [Model Context Protocol](https://about.gitlab.com/topics/ai/model-context-protocol/) and Agent2Agent, which simplify data access and improve how agents work together, increase security complexity because they expand the attack surface and create new pathways for unauthorized access across interconnected systems.\n\nHowever, these challenges shouldn’t stop security leaders from prioritizing AI governance. Organizations waiting for comprehensive AI best practices will find themselves constantly behind the curve, and those that avoid AI altogether will still be exposed to AI risks through vendor relationships and unauthorized AI use within their environments.\n\n## Practical steps CISOs can take for AI agent readiness\nSecurity leaders should start by establishing AI observability systems that can track, audit, and attribute agent behaviors across all environments. Here are a few steps CISOs can take today to reduce AI risk and improve governance.\n\n### Establish identity policies that create accountability for agent actions\nAs AI systems proliferate, managing non-human identities will be just as critical as controlling human user access. [Composite identities](https://about.gitlab.com/blog/improve-ai-security-in-gitlab-with-composite-identities/) offer one solution by connecting AI agent credentials with the human users who direct them. This approach helps organizations to authenticate and authorize agents while maintaining clear accountability for their actions.\n\n### Implement comprehensive monitoring frameworks\nDevelopment, operations, and security teams require visibility into AI agent activities across various workflows, processes, and systems. Monitoring cannot stop at code repositories. Teams must track agent behavior in staging environments, production systems, connected databases, and all applications the agents can access.\n\n### Develop team AI capabilities\nAI literacy is now a must-have for security teams. In GitLab’s survey, 43% of respondents acknowledged a growing AI skills gap, and this is likely to expand unless technical leaders invest in team education. Training should cover model behavior, prompt engineering, and critical evaluation of model inputs and outputs.\n\nKnowing where models excel and where they underperform helps teams avoid unnecessary security risks and technical debt. For instance, models trained on anti-patterns effectively detect those specific issues but struggle with unfamiliar logic bugs. AI models that perform poorly in areas where security engineers or developers lack experience will leave security gaps that human professionals won’t be able to identify. One solution that can help is to ensure teams have sufficient expertise to validate AI outputs and catch potential errors.\n\nCISOs should consider dedicating a portion of learning and development budgets to continuous technical education. This [builds internal AI security expertise](https://about.gitlab.com/the-source/ai/from-vibe-coding-to-agentic-ai-a-roadmap-for-technical-leaders/), creating AI champions who can train colleagues and reinforce good practices.\n\n## Security benefits outweigh AI adoption risks\nProperly monitored and implemented AI actually enhances security outcomes. In fact, 45% of survey respondents ranked security as the top area where AI can add value for software development. When used to accelerate rather than replace human expertise, AI can democratize security knowledge across development teams by automating routine security tasks, providing intelligent coding suggestions, and offering security context within developer workflows.\n\nFor example, AI can explain vulnerabilities, enabling developers to resolve issues quickly without waiting for security team guidance. These capabilities help improve security outcomes, reduce risk exposure, and increase understanding between development and security teams.\n\nSuccess belongs to organizations that embrace AI — but do so carefully. Even imperfect foundational controls help teams adapt as conditions change. If the executives surveyed are right, the three-year clock is already ticking. Leaders who guide their teams toward the right AI use cases won't just minimize risk; they will gain a competitive advantage. After all, the security of your software is a core component of its quality.",[704,707,710,713,716],{"header":705,"content":706},"What percentage of executives expect AI agents to become standard practice?","89% of C-level executives surveyed expect AI agents will become the standard approach for building software within three years. Additionally, 91% of executives plan to boost their AI investments in software development over the next 18 months. However, 85% recognize that AI agents will introduce never-before-seen security challenges.",{"header":708,"content":709},"What are the top security concerns executives have about AI agents?","The primary risks identified by survey participants are cybersecurity threats at 52%, data privacy and security concerns at 51%, and governance challenges at 45%. These interconnected risks continue to evolve as AI technology advances, creating complex security implications for organizations.",{"header":711,"content":712},"How many organizations currently lack proper AI governance frameworks?","Nearly half of surveyed leaders report governance gaps in their organizations. 47% said their organizations haven't implemented regulatory-aligned governance around AI, and 48% lack internal policies. This governance gap creates adoption barriers despite the urgent need for AI integration in business operations.",{"header":714,"content":715},"What practical steps can CISOs take to prepare for AI agent security?","CISOs should establish identity policies that create accountability for agent actions through composite identities connecting AI credentials with human users. Implement comprehensive monitoring frameworks tracking agent behavior across all environments including staging, production, and connected databases. Develop team AI capabilities including prompt engineering and model evaluation skills.",{"header":717,"content":718},"How do security benefits compare to AI adoption risks for organizations?","Security benefits can outweigh adoption risks when AI is properly monitored and implemented. 45% of survey respondents ranked security as the top area where AI can add value for software development. AI can democratize security knowledge, automate routine tasks, provide intelligent coding suggestions, and explain vulnerabilities to help developers resolve issues quickly.",{},"/en-us/the-source/security/ai-agents-are-reshaping-software-what-cisos-need-to-know",{"config":722,"title":694,"description":695},{"noIndex":427},"ai-agents-are-reshaping-software-what-cisos-need-to-know","en-us/the-source/security/ai-agents-are-reshaping-software-what-cisos-need-to-know","GWKfSmPXw84SGvDRnBgunUqPToAkDw9Nx9BuwSK-lxs",{"id":727,"title":728,"body":6,"category":20,"config":729,"content":732,"description":6,"extension":26,"meta":758,"navigation":28,"path":759,"seo":760,"slug":762,"stem":763,"type":430,"__hash__":764},"theSource/en-us/the-source/security/speed-and-control-gitops-for-insurance-leaders.yml","Speed And Control Gitops For Insurance Leaders",{"layout":8,"template":409,"featured":427,"author":730,"sourceCTA":731},"jason-morgan","beginners-guide-to-gitops",{"title":733,"description":734,"date":735,"timeToRead":416,"heroImage":736,"keyTakeaways":737,"articleBody":741,"faq":742},"Speed and control: GitOps for insurance leaders","Discover how GitOps and enterprise CI/CD enable insurance companies to deploy fast while meeting strict regulatory compliance and audit requirements.","2025-09-25","https://res.cloudinary.com/about-gitlab-com/image/upload/v1758827423/hpvkk3b8mozeqhed6daf.png",[738,739,740],"Insurance companies can achieve fast development cycles while maintaining regulatory compliance by combining GitOps tools like FluxCD with enterprise CI/CD platforms like GitLab.","Storing all deployment configs in Git creates automatic audit trails, version control, and enforced approval workflows that satisfy regulators and eliminate manual documentation.","Modern pipelines can automatically enforce separation of duties, require approvals, and block deployments that don't meet compliance rules—making governance systematic, not optional.","In conversations with insurance technology leaders, one challenge consistently emerges: How do you enable development teams to move at the speed modern customers expect while satisfying regulators who demand every change be tracked, approved, and reversible?\n\nThe answer isn't choosing between speed and control; it's combining the right tools to get both. That's where pairing GitOps tools like FluxCD with enterprise CI/CD platforms like GitLab creates something special: a deployment pipeline that's both developer-friendly and maintains the audit trails regulators require.\n\n## Why GitOps matters for insurance\n\nIf you're managing Kubernetes deployments in a regulated environment, you already know that \"just SSH in and fix it\" isn't an option. FluxCD and similar GitOps tools fundamentally change how we think about configuration management, and honestly, it's about time.\n\n### Everything lives in Git (where it belongs)\n\nWith FluxCD, your entire deployment configuration becomes code. Real, version-controlled, reviewable code. No more mystery configurations that changed three months ago and were never documented. Every YAML file, every Helm chart, every configuration parameter lives in Git repositories where they're subject to the same controls as your application code.\n\nThis isn't just about organization (though your future self will thank you during the next state insurance audit). When you treat configuration as code, you inherit all the battle-tested controls that software teams have refined over decades. Branch protection rules, pull request reviews, and signed commits aren't just for your Java or Python files anymore.\n\n### Your project becomes the single source of truth\n\nHere’s where compliance teams take notice: GitOps continuously monitors declared states and ensures clusters match what’s approved. Any drift between what’s intended and what’s running is automatically detected and reconciled.\n\nThis means your project isn't just documentation of what you think is running, it's the enforced state of your entire system. When an auditor asks, \"What version of this service was running on March 15th at 2 PM?\" you don't scramble through logs. You check the Project history. Simple, verifiable, and impossible to argue with.\n\n## Making GitOps enterprise-ready\n\nNow, having everything in Git is great, but insurers need more than just version control. They need to prove that every change followed proper procedures, met security requirements, and links to an approved business justification. This is where organizations must extend GitOps with a robust CI/CD system.\n\n### Change management that actually works\n\nInsurance CIOs and CTOs consistently cite manual change management processes as a major operational bottleneck. Their teams waste countless hours updating tickets, chasing approvals, and documenting deployments that should be automatic. Modern CI/CD pipelines solve this by integrating directly with change management systems, automatically creating and updating tickets as code moves through the deployment pipeline.\n\nEven better, these pipelines can enforce compliance rules:\n\n* Need actuarial approval for rating algorithm updates? The pipeline won’t proceed without it.\n* Require compliance review for underwriting logic? The deployment halts until sign-off.\n\nThis isn’t security theater — it’s real enforcement, applied consistently and automatically.\n\n### Separation of duties made simple\n\nInsurance regulators, whether state departments or international bodies like EIOPA, emphasize the separation of duties. The person who writes the code for premium calculations shouldn't be the one who approves it for production. Modern CI/CD platforms make this straightforward to implement and, more importantly, impossible to bypass.\n\nDevelopers can push code all day long, but they can't approve their own merge requests. They can't trigger production deployments without passing the necessary control gates. They can't modify audit logs. These aren't suggestions or guidelines; they're system-enforced rules that work across your entire development lifecycle.\n\n### A policy engine that speaks \"compliance\"\n\nThis is where [enterprise CI/CD platforms](https://about.gitlab.com/blog/ultimate-guide-to-ci-cd-fundamentals-to-advanced-implementation/) really earn their keep in insurance environments. Based on implementations I've overseen, the most successful platforms include comprehensive policy engines that can enforce virtually any requirement your compliance team requires:\n\n* **Permissions that make sense**: Role-based access control that maps to your actual organizational structure, not some generic \"admin/user\" split\n* **Audit trails that tell the whole story**: Not just who did what, but why they did it, who approved it, and what controls were validated\n* **Artifact management that satisfies regulators**: Automatic retention of build artifacts, deployment manifests, and security scan results for whatever period your regulations require\n* **Change window enforcement**: Block deployments during freeze periods, require additional approvals for emergency changes, or restrict certain types of changes to specific maintenance windows\n\n## GitOps and insurance: Better together\n\nIn my recent engagements with insurers ranging from regional carriers to global reinsurers, I've observed a clear pattern in successful GitOps adoptions. The magic happens when you pair GitOps approaches with enterprise controls, creating a deployment pipeline that developers actually want to use and that satisfies insurance compliance teams.\n\nDevelopers get to work with familiar Git workflows. They push code for new coverage types, create merge requests for claims automation improvements, and see their changes automatically deployed. No special deployment tools to learn, no manual steps to forget, no \"works in my machine\" mysteries when the new mobile claims app behaves differently in production.\n\nMeanwhile, your governance teams — who in insurance often report directly to the board's risk committee — get comprehensive audit trails, enforced approval workflows, and the ability to prove compliance without manual documentation. Every deployment is traceable from commit to production, with all the required approvals and security scans documented along the way.\n\nThe result? Your most advanced teams can iterate quickly, deploy frequently, and innovate confidently, all while maintaining the iron-clad controls that financial services require. It's not about choosing between moving fast and maintaining control. With the right tooling, you genuinely can have both.\n\n## Ready to see this in action?\n\nIf you're curious about how this approach could work in your organization, we're bringing the [Financial Services Roadshow](https://about.gitlab.com/events/financial-services-roadshow/) to several cities in the coming months. You'll see real-world implementations, hear from organizations that have made this transition, and get hands-on experience with the tools and workflows discussed here.",[743,746,749,752,755],{"header":744,"content":745},"How does GitOps help insurance companies balance speed and regulatory compliance?","GitOps enables insurance companies to deploy fast while meeting strict compliance requirements by combining tools like FluxCD with enterprise CI/CD platforms. All deployment configurations become version-controlled code in Git repositories, creating automatic audit trails and enforced approval workflows. This approach satisfies regulators while enabling developer-friendly deployment pipelines.",{"header":747,"content":748},"What makes GitOps configuration management suitable for regulated insurance environments?","GitOps treats entire deployment configurations as real, version-controlled, reviewable code stored in Git repositories. Every YAML file, Helm chart, and configuration parameter is subject to the same controls as application code, including branch protection rules and pull request reviews. This creates a single source of truth that's continuously monitored and automatically reconciled.",{"header":750,"content":751},"How do modern CI/CD pipelines enforce separation of duties for insurance compliance?","CI/CD platforms make separation of duties system-enforced rules rather than guidelines. Developers can push code but cannot approve their own merge requests or trigger production deployments without passing control gates. The person writing premium calculation code cannot approve it for production, and nobody can modify audit logs or bypass necessary approvals.",{"header":753,"content":754},"What compliance features do enterprise CI/CD platforms provide for insurance companies?","Enterprise platforms include comprehensive policy engines with role-based access control mapping to organizational structures, complete audit trails showing who did what and why with approval documentation, automatic retention of build artifacts and security scan results, and change window enforcement that blocks deployments during freeze periods.",{"header":756,"content":757},"How does storing deployment configurations in Git benefit insurance audits?","When deployment configurations live in Git, every change is tracked with complete version history, making audits straightforward. Instead of scrambling through logs when auditors ask about system states on specific dates, teams can check Git project history for simple, verifiable, and impossible-to-argue-with documentation of exactly what was running when.",{},"/en-us/the-source/security/speed-and-control-gitops-for-insurance-leaders",{"config":761,"title":733,"description":734},{"noIndex":427},"speed-and-control-gitops-for-insurance-leaders","en-us/the-source/security/speed-and-control-gitops-for-insurance-leaders","x8VwqQIO_oD5Aus0U1uo_Ve4JadCSYG6vRSfq92ZT-I",{"id":766,"title":767,"body":6,"category":20,"config":768,"content":770,"description":6,"extension":26,"meta":780,"navigation":28,"path":781,"seo":782,"slug":769,"stem":784,"type":785,"__hash__":786},"theSource/en-us/the-source/security/building-resilient-software-through-secure-development.yml","Building Resilient Software Through Secure Development",{"layout":8,"template":409,"featured":427,"gatedAsset":769},"building-resilient-software-through-secure-development",{"title":771,"description":772,"date":773,"heroImage":774,"keyTakeaways":775,"articleBody":779},"Building resilient software through secure development","Discover how to automate compliance, reduce security risks, and build resilient software. Learn proven strategies for integrating security into your SDLC.","2025-09-22","https://res.cloudinary.com/about-gitlab-com/image/upload/v1761157735/hfazekmlyinw8pvxcm2r.png",[776,777,778],"With 80% of Chief Compliance Officers foreseeing escalating compliance pressures, automating security processes throughout your development lifecycle is critical for maintaining competitive advantage and meeting evolving standards.","Organizations implementing automated compliance solutions eliminate manual audit tasks, allowing developers to focus on innovation while security and governance controls operate seamlessly in the background.","Modern DevSecOps platforms enable organizations to enforce compliance directly in CI/CD pipelines, providing comprehensive audit trails, vulnerability management, and provenance tracking required by federal standards.","In today's threat landscape, software vulnerabilities can swiftly escalate to national security issues. Foreign adversaries conduct sophisticated cyber campaigns costing billions of taxpayer dollars while undermining organizational security and privacy. With Executive Order 14306 reinforcing the government's commitment to secure software development and strengthening NIST's Secure Software Development Framework as the definitive best practice, the question isn't whether to prioritize security, it's how to implement it effectively.\n\n## The challenge: Speed vs. security\nHistorically, organizations have prioritized development speed at the expense of security, leaving critical vulnerabilities in their products. This trade-off became more prominent with widespread DevOps adoption, as rapid release cycles often outpaced security considerations. Manual compliance tracking pulls developers away from core development work, with teams spending significant time on audit tasks and regulatory documentation.\n\nOrganizations navigating multiple compliance frameworks (NIST, FedRAMP, FISMA, ISO 27001, SOC 2) face an even greater challenge. While these frameworks share common controls, they rarely align perfectly, creating manual tracking burdens that scale poorly across complex development environments.\n\n## A strategic approach to embedded security\nThe path forward requires more than checkbox compliance. Organizations that proactively embed compliance requirements into development processes from the outset realize significant competitive advantages, time savings, and cost efficiencies. This means codifying standards and seamlessly integrating security throughout the software development lifecycle rather than treating it as a final gate.\n\nEffective implementation demands automated guardrails that enforce security policies without slowing development velocity. Protected branches, merge request approvals, and automated scanning ensure code stability while maintaining rapid delivery cycles. Security policies act as automated safeguards throughout the software development lifecycle, enforcing specific security actions at each pipeline stage.\n\n## Visibility and control across the supply chain\nModern development environments require answers to fundamental questions: What assets do we have? Are they being scanned? Where are we most at risk? Software bill of materials generation, dependency scanning, and continuous vulnerability monitoring provide the visibility needed to manage risk across sprawling codebases.\n\nStatic reachability analysis enables teams to prioritize remediation based on actual threat exposure rather than scanning all vulnerable dependencies. Comprehensive vulnerability risk assessment data, including EPSS scores and Known Exploited Vulnerabilities status, allows teams to focus on real-world threats.\n\n## From principle to practice\nThe Principle of Least Privilege, developed in the 1970s, remains fundamental to modern security. Implementing sophisticated role-based access control ensures each user and system has precisely the permissions required for designated responsibilities. Fine-grained permissions for both human users and non-human identities minimize blast radius if credentials are compromised.\n\nOrganizations that successfully navigate today's compliance landscape don't treat security as an afterthought. They embed it into every stage of development, automate verification processes, and maintain continuous monitoring. This comprehensive approach transforms compliance from a burden into a competitive advantage.\n\n**Download the complete guide to learn how leading organizations can automate compliance, implement secure guardrails, and build truly resilient software.**",{},"/en-us/the-source/security/building-resilient-software-through-secure-development",{"config":783,"title":771,"description":772},{"noIndex":427},"en-us/the-source/security/building-resilient-software-through-secure-development","guide","TgSsnoMelUOj0999RUDkqQUEl4K7M2shao-EnfeR4_M",[788,801,814],{"id":567,"title":568,"body":6,"category":25,"config":789,"content":790,"description":6,"extension":26,"meta":798,"navigation":28,"path":599,"seo":799,"slug":602,"stem":603,"type":430,"__hash__":604},{"layout":8,"template":409,"featured":28,"author":570,"sourceCTA":571},{"title":573,"description":574,"date":575,"timeToRead":416,"heroImage":576,"keyTakeaways":791,"articleBody":581,"faq":792},[578,579,580],[793,794,795,796,797],{"header":584,"content":585},{"header":587,"content":588},{"header":590,"content":591},{"header":593,"content":594},{"header":596,"content":597},{},{"config":800,"title":573,"description":574},{"noIndex":427},{"id":606,"title":607,"body":6,"category":25,"config":802,"content":803,"description":6,"extension":26,"meta":811,"navigation":28,"path":638,"seo":812,"slug":641,"stem":642,"type":430,"__hash__":643},{"layout":8,"template":409,"featured":427,"author":609,"sourceCTA":534},{"title":611,"description":612,"date":613,"timeToRead":614,"heroImage":615,"keyTakeaways":804,"articleBody":620,"faq":805},[617,618,619],[806,807,808,809,810],{"header":623,"content":624},{"header":626,"content":627},{"header":629,"content":630},{"header":632,"content":633},{"header":635,"content":636},{},{"config":813,"title":611,"description":612},{"noIndex":427},{"id":815,"title":816,"body":6,"category":25,"config":817,"content":819,"description":6,"extension":26,"meta":829,"navigation":28,"path":830,"seo":831,"slug":818,"stem":833,"type":785,"__hash__":834},"theSource/en-us/the-source/platform/a-guide-to-scm-platform-selection-for-government.yml","A Guide To Scm Platform Selection For Government",{"layout":8,"template":409,"featured":427,"gatedAsset":818},"a-guide-to-scm-platform-selection-for-government",{"title":820,"description":821,"date":822,"heroImage":823,"keyTakeaways":824,"articleBody":828},"A guide to SCM platform selection for government","Identify the best SCM platform for your agency with a framework that identifies pain points, evaluates vendors, and provides implementation stages. ","2025-10-31","https://res.cloudinary.com/about-gitlab-com/image/upload/v1763134761/qybmsxddsltearogxxet.png",[825,826,827],"Government agencies need an SCM platform that can balance speed and complex security requirements. ","Before selecting a platform, assess current paint points across all teams that influence source code including, developers, security, operations, and leadership.","When evaluating vendors, keep the public sector’s unique needs in mind, such as collaborative features that work with security clearance levels or air-gapped functionality for classified environments.","Government agencies operate under unique constraints: teams must meet stringent security mandates, adhere to compliance and regulatory requirements, and closely track spending for budget accountability. When coupled with toolchain sprawl and poorly integrated interfaces, these constraints create significant challenges: slow delivery velocity, rising operational costs, and increased security vulnerabilities.\n\n\nThe right source code management (SCM) platform can alleviate many of these challenges, which is why we developed a comprehensive framework to help the public sector evaluate and implement the right SCM solution.\n\n## Why an SCM platform matters\nSCM platforms are the single source of truth for code changes, tracking who changed what code, when it was changed, and why. This creates an audit trail that’s beneficial to team collaboration, troubleshooting vulnerabilities, and compliance. The right SCM platform accelerates the delivery of citizen services from weeks to days and enables teams to find vulnerabilities before they reach production.\n\n## Is your current SCM solution holding you back?\nBefore jumping into a new platform, first identify the main pain points across your teams. Here are a few example questions you could ask each team:\n\n* **Developers**: How many tools do you log into daily to get code from idea to production? Which steps in the development process create the most delays for deployments? \n* **Security**: How early in the development process does your team get involved? How many different security tools are you using today?\n* **Operations**: How do you currently get visibility into what's being deployed, by whom, when, and whether it's running successfully across all your\nenvironments?\n* **Leadership**: How much are you spending annually on your software development toolchain, (including integration maintenance, support contracts, and engineering time)?\n\n## The top criteria to evaluate SCM vendors\nBecause the public sector has such specific needs, the questions that you ask when evaluating SCM vendors should explore its unique requirements. In particular, teams should ask potential vendors about:\n\n* **Tool integration and consolidation**: Every tool integration creates ongoing maintenance overhead and potential security vulnerabilities. Understand how a platform integrates or replaces DevSecOps tools and CI/CD pipelines to determine if the investment is worthwhile.\n* **Collaborative features**: Government development involves multiple locations, varying security clearance levels, and complex approval processes. Review how the platform enables workflows with features like approval chains and audit logging.\n* **Government-specific security requirements**: The public sector operates across diverse security environments, often requiring self-managed deployments or air-gapped functionality. Dive into a platform’s security capabilities to identify the right solution for your organization. \n* **Trusted AI**: AI increases efficiency for resource-constrained government teams, but agencies must maintain security boundaries. Ask about security protections that safely enable AI features.\n* **Migration support**: Vet vendors’ professional services, such as government migration experience, automated import tools with full history preservation, and support for complex security clearance requirements.\n\n## Finding the right SCM solution for your organization\nGovernment organizations need an SCM platform that can accelerate development while maintaining strict security requirements. To find the best platform for your organization, you need to know what questions to ask, what capabilities to look for, and how it will be implemented. 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