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AI Is Making Developers Faster — But Security Has a New Problem
Artificial intelligence is rapidly changing how software is built. Developers can now generate functions, automate repetitive tasks, troubleshoot errors, write tests, and move from an idea to working code in a fraction of the time it once required.
The Productivity Revolution Has a Cost
That acceleration is exciting for engineering teams, but it creates a less visible challenge for cybersecurity leaders: AI-generated code can dramatically increase the number of open-source components entering an organization’s technology environment.
The Dependency Problem Is Growing
A developer working with an AI coding assistant can add a package or dependency almost instantly. What looks like a small productivity win, however, can create a much larger responsibility for security and engineering teams.
Every New Package Creates New Questions
Once a dependency enters a software project, someone has to determine whether it contains known vulnerabilities, whether it is actively maintained, who controls it, what license it uses, whether its source can be trusted, and whether the organization actually needs it.
Faster Development Does Not Mean Faster Remediation
AI can accelerate the creation of software, but it does not automatically accelerate the security processes surrounding that software. This creates a dangerous imbalance: developers can produce and integrate components faster than security teams can review and remediate them.
The Rise of Remediation Debt
This growing backlog can be described as remediation debt — security work that accumulates because vulnerabilities and dependencies are being introduced faster than organizations can properly address them.
Why Remediation Debt Matters
Like technical debt, remediation debt can remain relatively invisible while a development organization is moving quickly. The consequences become clearer later, when vulnerabilities pile up, audits become more difficult, engineers spend increasing amounts of time fixing old problems, and security teams struggle to determine what deserves immediate attention.
AI Could Magnify an Existing Security Challenge
The underlying problem is not necessarily AI itself. Open-source security challenges existed long before generative AI became mainstream. The difference is that AI coding tools can increase the speed and scale at which software and dependencies are introduced.
The More Autonomous AI Becomes, the Bigger the Challenge Could Get
Today’s AI coding assistants already generate substantial amounts of code. As development agents become capable of making broader decisions, modifying repositories, selecting libraries, running tests, and completing larger portions of development workflows, the number of decisions being made outside traditional human review could increase dramatically.
Security Teams Were Not Built for Infinite Velocity
Most security programs were designed around a development environment where changes could be reviewed, prioritized, scanned, tested, and remediated within established workflows.
AI Changes the Speed of the Pipeline
When software production accelerates, those same controls can become bottlenecks. If the security process remains unchanged while development becomes dramatically faster, the organization may simply produce security findings faster than it can resolve them.
The 300-Leader Benchmark
The original ActiveState research highlighted in the source article surveyed 300 security and engineering leaders across industries including technology, financial services, healthcare, manufacturing, and government.
Why Cross-Industry Data Matters
A benchmark covering multiple industries can provide useful context because organizations face very different regulatory, operational, and security requirements. A financial institution may approach open-source governance differently from a manufacturing company, while healthcare organizations may face another layer of compliance pressure.
The Real Value of Benchmarking
For security leaders, the most useful question is not simply whether AI introduces risk. The more important question is whether their organization is managing that risk better or worse than comparable enterprises.
Are Security Controls Keeping Up?
A company may believe its dependency-management program is effective because vulnerabilities are being identified. But discovery alone does not solve the problem.
Detection Is Only the Beginning
A vulnerability becomes a business problem when an organization cannot determine its exposure, prioritize the issue, identify affected applications, assign ownership, and complete remediation within an acceptable timeframe.
The Backlog Can Become the Bigger Threat
A growing backlog creates a difficult paradox. Security teams may have excellent scanning capabilities while still becoming less secure overall if the number of unresolved findings continuously increases.
AI Can Change the Shape of That Backlog
AI-generated development can introduce dependencies through legitimate engineering decisions, experimental code, automated suggestions, and rapid prototyping. Some of those components may eventually disappear, while others can become deeply embedded in production applications.
Temporary Code Can Become Permanent Infrastructure
One of the most underestimated risks of fast AI-assisted development is that code initially created for experimentation can survive much longer than expected.
Open Source Is Not the Enemy
Open-source software remains one of the foundations of modern computing. Virtually every major software organization relies on open-source components in some form.
The Issue Is Governance, Not Adoption
The question is therefore not whether organizations should stop using open-source software. The better question is whether they can understand and control the open-source components entering their environments at the speed AI-assisted development demands.
Developers Need Security Guardrails, Not Roadblocks
Security programs that respond to AI adoption by simply blocking developers are unlikely to succeed. Developers will continue looking for ways to move quickly.
Automation Is Becoming Essential
The more effective approach is to automate as much of the security lifecycle as possible. Dependency discovery, vulnerability identification, prioritization, license analysis, software inventory management, and remediation workflows should increasingly operate as part of the development pipeline.
Context Matters More Than Raw Vulnerability Counts
Not every vulnerability represents the same level of danger. A critical vulnerability in an internet-facing production service can be dramatically more urgent than a low-severity issue in an isolated development environment.
AI Security Requires Better Prioritization
Security teams therefore need more than a list of CVEs. They need context about where vulnerable packages are deployed, how they are being used, whether exploitation is known, what data the affected application can access, and how important that application is to the business.
Ownership Is Another Major Challenge
A vulnerability without a clearly responsible owner can remain unresolved indefinitely. Organizations need clear accountability for dependencies and applications so that security findings do not disappear into an organizational gray area.
The Developer Experience Matters
Security controls that slow development excessively can create resistance. The strongest programs make the secure path the easiest path.
Secure Defaults Can Change the Equation
If approved packages, trusted repositories, automated scanning, dependency policies, and remediation recommendations are built directly into development workflows, developers can receive security guidance without having to stop what they are doing.
Governance Must Evolve With AI
Traditional governance models may assume that a human developer deliberately selects most components entering a project. AI-assisted development complicates that assumption.
The Human Role Is Changing
Developers may increasingly become reviewers, architects, and decision-makers rather than manually producing every line of code. That means organizations need governance that evaluates not only who wrote the code, but also how dependencies and design decisions were introduced.
Security Teams Need Visibility Into AI-Assisted Development
Organizations should understand where AI coding tools are being used, what repositories they can access, what permissions they have, and how generated code enters production systems.
AI Governance and Open-Source Governance Are Converging
These were once treated as separate conversations. Increasingly, they are becoming connected.
The Software Supply Chain Is Expanding
Every external package, model, plugin, library, API, and automated development action can become part of an organization’s broader software supply chain.
Attackers Understand This Opportunity
Cybercriminals have long targeted software supply chains because compromising one trusted component can provide access to many downstream organizations.
AI Creates More Opportunities for Mistakes
The concern is not only malicious activity. AI systems can confidently recommend outdated, vulnerable, unnecessary, or poorly maintained components if the surrounding development process does not provide adequate controls.
Human Review Still Matters
AI coding systems can be extremely useful, but organizations should avoid treating generated code as automatically trustworthy simply because it was produced by a sophisticated model.
Security Must Become Part of the Development Loop
The strongest strategy is to move security checks closer to the moment when developers make decisions. Waiting until the end of a project to discover dependency problems is increasingly incompatible with AI-powered development.
The
The ActiveState webinar described in the original article focuses on precisely this tension: AI coding can increase development productivity while simultaneously increasing the workload associated with open-source risk and remediation.
What Enterprise Leaders Should Be Asking
Security leaders should ask whether their teams can identify AI-generated dependencies, whether they know which applications contain them, how quickly vulnerabilities are remediated, and whether their existing governance model can scale as AI adoption grows.
The Biggest Mistake Would Be Waiting
Organizations that wait until AI-generated software becomes deeply embedded across their environments may discover that retrofitting security controls is far more expensive than building them into development workflows from the beginning.
Deep Analysis
The AI Productivity Paradox
AI coding tools create a genuine productivity opportunity, but productivity without corresponding security capacity can simply move the bottleneck downstream.
Speed Is Not the Same as Efficiency
If developers produce software twice as quickly but security teams inherit twice as much remediation work, the organization has not necessarily achieved twice the overall efficiency.
Dependency Growth Deserves Attention
The expansion of software dependencies should be treated as a measurable security and operational metric, particularly in organizations experiencing rapid AI adoption.
Security Debt Can Compound
Unresolved vulnerabilities rarely exist in isolation. A growing backlog can make prioritization increasingly difficult and consume resources that could otherwise be spent on proactive security improvements.
AI Changes the Economics of Development
The cost of generating code is falling. As that happens, organizations may create more software than ever before.
More Software Means More Security Responsibility
Every application requires maintenance, monitoring, patching, dependency management, and eventually retirement. AI can reduce the cost of creation without eliminating those long-term obligations.
Open Source Will Remain Central
There is little reason to expect organizations to move away from open-source software. Its flexibility, availability, and enormous developer ecosystem make it fundamental to modern engineering.
The Winning Strategy Is Better Control
Organizations do not need to eliminate open source. They need stronger visibility, better governance, faster remediation, and smarter prioritization.
Vulnerability Scanning Alone Is Insufficient
A scanner can identify thousands of issues, but a security team still needs to determine which findings matter most and what action should follow.
Prioritization Becomes Critical
AI-driven development could make raw vulnerability counts increasingly meaningless unless organizations can connect security findings to actual business risk.
Software Bills of Materials Become More Valuable
An accurate software inventory can help organizations understand what components exist inside applications and respond more quickly when a vulnerable package is discovered.
Automated Remediation Has Greater Potential
As AI-assisted development becomes more advanced, automated remediation could become an important counterbalance to automated code generation.
Automation Must Be Controlled
Automatically changing production dependencies without adequate testing could introduce new problems. Remediation automation therefore needs guardrails, testing, rollback capabilities, and clear ownership.
Security Teams Should Measure Remediation Velocity
The number of vulnerabilities detected tells only part of the story. How quickly critical findings are resolved may be a more meaningful indicator of security maturity.
Development and Security Metrics Should Connect
Organizations should increasingly evaluate development velocity alongside vulnerability creation, remediation speed, dependency growth, and security exceptions.
Security Cannot Become the Department of No
If security teams simply block AI coding tools, developers may find alternative workflows that provide less visibility.
Collaboration Is More Sustainable
Security, engineering, and development-platform teams should establish shared policies that allow AI adoption while maintaining meaningful controls.
AI Agents Need Stronger Permissions
As AI systems become more autonomous, permission management will become increasingly important. An AI assistant with access to a repository should not automatically receive unrestricted access to production infrastructure.
Least Privilege Matters More Than Ever
AI agents should receive only the permissions necessary for the task they are performing. Limiting access reduces the potential consequences of both mistakes and compromise.
The Supply Chain Will Become More Dynamic
Software environments are likely to change more frequently as AI systems continuously generate, modify, and optimize code.
Static Governance Models May Struggle
A policy reviewed once per quarter may not be sufficient for an environment changing dozens or hundreds of times every day.
Real-Time Security Is Becoming More Important
Security controls will increasingly need to operate continuously rather than relying entirely on periodic audits and manual reviews.
Compliance Will Feel the Pressure
As AI-generated code becomes more common, auditors and regulators may increasingly ask organizations to demonstrate how AI-assisted development is governed and secured.
Productivity Claims Need Security Context
Organizations should be careful when measuring the success of AI coding programs. Lines of code generated or developer hours saved do not necessarily translate into business value if security and maintenance costs rise sharply.
The Real Metric Is Sustainable Velocity
The strongest organizations will not necessarily be those generating the most code. They will be those capable of increasing development speed without allowing quality, security, and operational debt to grow uncontrollably.
AI Adoption Is Becoming a Security Transformation
This issue should not be treated as a temporary challenge associated with a new developer tool. AI is changing how software is produced, reviewed, tested, and maintained.
The Security Architecture Must Adapt
Organizations that successfully integrate AI into development will likely combine automated dependency intelligence, continuous scanning, strong identity controls, secure development environments, policy enforcement, and human oversight.
The Next Stage Will Be More Autonomous
As AI coding agents take on larger portions of development, organizations will need to decide which actions can be automated and which decisions should always require human approval.
The Biggest Risk May Be Invisible Growth
The most dangerous outcome may not be one catastrophic vulnerability. It could be thousands of small dependencies and security exceptions quietly accumulating until the organization can no longer manage them effectively.
The Strategic Opportunity
AI gives enterprises an opportunity to rethink security processes rather than simply adding more manual work to an already overloaded team.
The Final Takeaway
The central lesson from the article is straightforward: AI-assisted development can dramatically accelerate software creation, but security programs must accelerate as well.
What Undercode Say:
AI Is Not the Villain
AI coding tools are not inherently the problem. They are amplifiers. They can amplify developer productivity, but they can also amplify weak dependency-management practices.
The Real Bottleneck Is Remediation
The most important issue is not how many vulnerabilities an organization discovers. It is whether the organization can consistently resolve the vulnerabilities that actually matter.
Speed Creates a New Security Equation
When development becomes faster, security cannot remain a largely manual afterthought. The security process has to match the velocity of the development process.
Open Source Needs Visibility
Organizations should know what packages are being used, where they are deployed, who owns them, and how important they are to business operations.
AI Makes Visibility Harder
When developers can generate and modify software rapidly, traditional asset inventories can become outdated quickly.
Automation Is the Obvious Answer
Automating discovery and prioritization can help security teams manage the increased volume without simply hiring more people to perform repetitive tasks.
Human Judgment Still Matters
Automation should handle repetitive work, while experienced security professionals focus on high-impact decisions and unusual threats.
Dependency Policies Should Be Practical
Security policies should not make legitimate development unnecessarily difficult. Developers need clear rules and convenient secure alternatives.
AI Governance Must Include Supply-Chain Risk
Organizations discussing responsible AI adoption should include the software supply chain in that conversation.
Security Teams Need a Seat at the AI Table
AI adoption should not be treated exclusively as an engineering or productivity initiative. Security needs to participate from the beginning.
The Backlog Is a Warning Signal
A rapidly increasing remediation backlog should be considered an organizational warning sign, especially when AI adoption is accelerating simultaneously.
Vulnerability Numbers Can Be Misleading
A company with thousands of findings is not necessarily less secure than one with hundreds. Context, exploitability, exposure, and remediation performance matter.
Business Impact Should Drive Priorities
Security teams should focus resources where compromise could cause meaningful operational, financial, regulatory, or reputational damage.
Developers Need Better Feedback
AI-assisted development creates an opportunity to provide developers with immediate information about insecure dependencies before those dependencies become deeply integrated.
Security Can Move Left Without Becoming Manual
Modern security should not mean adding another checklist for developers. It should mean embedding automated controls directly into the tools developers already use.
AI Agents Require Guardrails
The more autonomous development agents become, the more important permissions, audit logs, approval mechanisms, and sandboxing will become.
Trust Must Be Earned
Generated code should be treated as something to evaluate, not something to automatically trust.
Software Creation Is Becoming Cheap
As AI reduces the effort required to create software, organizations will need to become more disciplined about deciding which software should actually be maintained.
More Code Is Not Always Better
Producing more code can create additional maintenance and security obligations. The goal should be useful, reliable, secure software — not maximum code output.
Remediation Velocity Could Become a Competitive Advantage
Companies that can identify and fix vulnerabilities quickly may be able to adopt AI development tools more aggressively without accepting proportionally greater security exposure.
Security and Engineering Metrics Should Converge
Organizations should measure development speed and security performance together rather than treating them as competing objectives.
The Future Belongs to Integrated Workflows
The strongest development environments will likely combine coding assistance, dependency intelligence, automated testing, security scanning, policy enforcement, and remediation recommendations.
AI Will Keep Increasing Development Velocity
There is little reason to expect the AI coding trend to reverse. The technology will likely become more capable and more deeply embedded in development workflows.
Security Teams Must Prepare Before That Happens
Waiting until AI agents control large portions of the software lifecycle could make it much harder to introduce appropriate controls.
Remediation Debt Could Become a Board-Level Issue
If vulnerability backlogs begin affecting audits, productivity, incident exposure, or regulatory compliance, remediation can no longer be considered merely a technical concern.
The Best Security Program Is Scalable
A process that works for a small development team may collapse when applied to thousands of AI-assisted developers and millions of generated lines of code.
AI Demands Continuous Governance
Security policies will need to evolve as AI tools, models, agents, permissions, and development practices change.
The Supply Chain Is Becoming More Complex
Organizations should expect their software supply chains to become increasingly dynamic, interconnected, and difficult to track manually.
Security Automation Will Become Essential Infrastructure
Automated vulnerability management and dependency intelligence may eventually become as fundamental to software development as source control and automated testing.
The Biggest Opportunity Is Integration
Rather than treating AI security as a separate program, enterprises can integrate AI governance into existing application-security and software-supply-chain processes.
The Biggest Mistake Is Ignoring the Backlog
If organizations celebrate faster development while ignoring growing remediation debt, the productivity gains may eventually be undermined by security and maintenance costs.
Sustainable AI Development Is the Goal
The future of AI-assisted programming should not be measured by how quickly software can be produced alone. It should be measured by how safely and sustainably that software can be operated.
✅ The source article states that ActiveState surveyed 300 security and engineering leaders across technology, financial services, healthcare, manufacturing, and government. This figure should be treated as a claim from the underlying research unless the original study methodology and published dataset are independently reviewed.
✅ The article’s central argument is technically plausible: AI-assisted development can increase the volume and speed of code and dependency creation, potentially increasing the workload for software composition analysis and vulnerability remediation.
⚠️ Claims connecting remediation debt directly to audit failures, breach frequency, and lost productivity require the underlying ActiveState research to verify the exact statistical relationship. The supplied article references those findings but does not provide the supporting numbers or methodology.
Prediction
(+1) AI-assisted development will continue expanding across enterprise software teams, making automated dependency discovery, vulnerability prioritization, and remediation increasingly important components of modern development pipelines.
(+1) Organizations that integrate security controls directly into AI-assisted coding workflows will have an advantage over companies that rely heavily on manual reviews after code has already entered production.
(+1) Software supply-chain security is likely to become a larger part of AI governance as organizations deploy increasingly autonomous coding agents with access to repositories, packages, development environments, and potentially production systems.
(-1) Organizations that dramatically increase AI-generated software without increasing security and remediation capacity could see vulnerability backlogs grow faster than their teams can manage, creating a form of security debt that becomes progressively more expensive to resolve.
(-1) If enterprises measure AI success primarily through development speed while ignoring dependency quality, vulnerability exposure, maintenance costs, and remediation performance, some of the apparent productivity gains could eventually be offset by growing operational and security burdens.
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