The New Bottleneck: Why Security Verification Can't Keep Up with AI-Accelerated Development

TL;DR

AI coding assistants have increased development velocity by 30-50% at many organizations. But security verification, the process of confirming that controls work, evidence exists, and findings are real, remains almost entirely human-paced. The result is a growing structural mismatch that more scanning tools cannot solve. Companies need to rethink how verification itself is performed.

The Mismatch Nobody Planned For

Something changed in software development over the past two years, and security teams felt it before anyone could name it.

Engineering teams adopted AI coding assistants like GitHub Copilot, Cursor, and Amazon Q Developer. Code output accelerated. Deployment frequency increased. Feature backlogs shrank. From a product perspective, this was exactly the productivity gain the industry had been chasing.

But security workflows did not accelerate alongside development. The tools that find vulnerabilities got faster, yes. Static analysis runs in CI/CD pipelines. Dependency scanners flag known CVEs automatically. Cloud security posture management tools generate findings in real time. Detection has never been more capable.

The problem is everything that happens after detection.

Someone still has to review each finding, determine whether it is a true positive, assess the risk in context, decide on a remediation path, implement the fix, and verify the fix works. Someone still has to collect evidence that controls operated correctly this quarter. Someone still has to prepare documentation for the auditor, respond to security questionnaires, and coordinate penetration test schedules as release cadence increases.

This is the new bottleneck. Not finding issues, but verifying, contextualizing, and resolving them at the pace that modern development demands.

What AI Development Actually Changed

To understand the bottleneck, it helps to understand what AI coding tools did and did not change.

What accelerated

AI assistants increased the rate at which code is written, reviewed, and shipped. GitHub's own research found that developers using Copilot completed tasks up to 55% faster. A 2024 study by McKinsey reported that developers using generative AI tools saw productivity gains of 20-45% on coding tasks, depending on complexity.

But raw coding speed is only one piece of the picture. Bain & Company's analysis found that the meaningful productivity gains, on the order of 10-15%, only materialize when organizations redesign the entire software delivery lifecycle around AI-assisted workflows. Faster code generation without corresponding changes to review, testing, integration, and governance simply shifts the pressure downstream.

For engineering leaders, this translated into:

• Higher deployment frequency. Teams that deployed weekly started deploying daily.
• Larger pull requests. AI-generated code tends to produce more lines per change.
• More feature throughput. Product roadmaps accelerated as engineering capacity effectively increased.
• Faster prototyping. New services and microservices spun up more quickly, expanding the overall system surface area.
• Higher defect density in generated code. A 2024 evaluation by Georgetown's Center for Security and Emerging Technology (CSET) found that nearly half of code snippets produced by leading LLMs contained bugs with real security implications. More code, produced faster, with a non-trivial error rate, compounds the verification burden significantly.

What did not accelerate

Security verification remained human-paced. Specifically:

• Triage and context assessment. A scanner can flag a finding, but determining whether it matters in your specific architecture requires an engineer who understands the system. That engineer's capacity did not increase.
• Evidence collection for compliance. SOC 2 and ISO 27001 audits require documented evidence that controls operated effectively over an observation period. Collecting screenshots, pulling access logs, verifying configurations, and organizing artifacts is still a manual process at most organizations.
• Penetration testing. More releases mean more features to test. Pentest scoping, execution, and report review are human-dependent, and backlogs are growing. The most common vulnerabilities we find in SaaS applications, from broken access control to authentication flaws, each require human judgment to confirm and remediate.
• Security review in CI/CD. When security review becomes a gate in the pipeline, and deployment frequency doubles, that gate becomes the constraint. Security engineers become the bottleneck for merge approvals.
• Audit preparation. Organizations preparing for SOC 2 Type II audits still spend weeks gathering evidence across systems. The number of controls to verify has not decreased, but the pace of changes those controls must cover has increased.
• Authorization and business logic verification. The fastest-growing category of risk, broken object-level authorization, is described by OWASP as extremely common in API-driven applications. These flaws look like valid requests to scanners, because they are syntactically correct. Confirming whether an authorization boundary actually holds, or whether a multi-step workflow can be abused, requires manual testing against real application logic. As AI accelerates the creation of new API endpoints and business workflows, the volume of authorization logic that needs human verification grows in lockstep.

The net effect: detection capacity scaled, but verification capacity stayed flat. This created a widening gap.

The Math of the Verification Gap

The dynamics are straightforward. Consider a typical SaaS company with 30 engineers:

Before AI coding tools (2023):

• ~200 pull requests per month
• ~50 deployments per month
• ~120 scanner findings per quarter
• 1.5 security engineers handling triage, review, and compliance

After AI coding tools (2025-2026):

• ~350 pull requests per month
• ~100 deployments per month
• ~250 scanner findings per quarter
• 1.5 security engineers handling triage, review, and compliance

The engineering team's output increased by 75%. The scanner findings roughly doubled (more code, more dependencies, more infrastructure). But the security team did not grow. In many cases, it could not grow, because experienced security engineers are expensive and hard to hire. ISC2's 2024 Cybersecurity Workforce Study estimated a global shortfall of 4.8 million cybersecurity professionals.

This is not a tooling problem. It is a structural capacity problem.

Why Better Scanners Don't Solve It

The instinctive response to "too many findings" is to buy a better scanner, one that is smarter about false positives, better at prioritization, or faster at running. This helps at the margins, but it misses the core issue.

The detection-verification asymmetry

Detection is the easy part. Modern SAST, DAST, SCA, and CSPM tools are remarkably good at finding issues. The problem is that each finding, whether real or false positive, requires human time to process:

A scanner that reduces false positives by 20% helps. But the remaining 80% of findings still require the same human-intensive verification pipeline. And when the total volume of findings has doubled, a 20% reduction in false positives still leaves you with more work than you started with.

The consequences of slow verification are not theoretical. Verizon's 2025 Data Breach Investigations Report found that exploitation of vulnerabilities as an initial access vector grew by 34% year over year, now accounting for 20% of breaches, nearly matching credential abuse at 22%. Critically, only 54% of perimeter device vulnerabilities were fully remediated, with a median time-to-remediate of 32 days. Attackers are not waiting for security teams to work through their backlogs.

The compliance verification burden

The verification bottleneck is even more pronounced in compliance. SOC 2 audits and ISO 27001 certifications require evidence that controls operated effectively over time. This means:

• Access reviews must be documented quarterly. Every user, every system, every review cycle. The number of systems and users grows as the company grows, but the process remains manual at most organizations.
• Change management evidence must show that every production change was approved and reviewed. When deployment frequency doubles, the volume of evidence doubles. We see this pattern repeatedly in common SOC 2 audit exceptions: missing change approval documentation is one of the most frequent findings.
• Vulnerability management requires tracking findings to resolution within defined SLAs. More code and more infrastructure means more findings, each requiring triage, assignment, remediation, and verification.
• Incident response documentation must demonstrate that the organization detects and responds to events. More systems and faster changes increase the volume of events that need investigation.

No scanner automates any of this. These are verification and documentation tasks that consume security and engineering time disproportionately.

The Structural Issues AI Exposed

AI-accelerated development did not create these problems. It exposed structural weaknesses that were always present but manageable at slower delivery speeds.

1. Compliance was designed for a slower world

SOC 2 and ISO 27001 were designed when software shipped quarterly or annually. The audit model, observe controls over a period, collect evidence at the end, verify everything in a multi-week engagement, assumes a relatively stable environment. When the environment changes daily, the evidence collection burden grows proportionally, but the audit model does not adapt.

Organizations preparing for their first SOC 2 often discover that evidence collection alone takes weeks of engineering time. In a world of daily deployments, there are simply more changes to document, more access events to review, and more configurations to verify.

2. Security review as a serial bottleneck

Many organizations require security review before production deployment. This made sense when deployments happened weekly. When deployments happen multiple times per day, a human security review becomes a serial bottleneck, each deployment waits in queue for the same small set of reviewers.

The response is often to loosen security review requirements ("only review changes over X lines" or "skip review for non-critical services"), which trades throughput for coverage. This is not an improvement; it is an acknowledgment that the process does not scale.

3. Manual evidence collection creates compounding debt

Every quarter, someone at the organization gathers evidence for compliance: screenshots of configurations, exports of access logs, records of completed training, vulnerability scan results, penetration test reports. This work compounds. More systems, more users, more deployments all mean more evidence to collect.

Organizations that rely on manual evidence collection find that the effort grows with each audit cycle, not because the requirements change, but because the environment grows. What took two weeks of preparation in Year 1 takes four weeks in Year 3.

4. The talent bottleneck amplifies everything

All of this is compounded by the persistent shortage of security professionals. The Cybersecurity and Infrastructure Security Agency (CISA) has identified workforce development as a national priority. If security verification work grows by 50-100% but the available workforce does not, the gap becomes structural.

Organizations cannot simply hire their way out. Senior security engineers who can perform effective triage, conduct compliance programs, and review architectures are scarce and expensive. Even well-funded companies report multi-month hiring timelines for these roles.

What Forward-Looking Companies Are Doing

The organizations handling this transition effectively are not just buying more tools. They are restructuring how security verification works.

Shifting from periodic to continuous compliance

Instead of collecting evidence in a scramble before each audit, these organizations automate evidence collection so it happens continuously. Access review evidence is generated automatically when reviews complete. Change management evidence is captured by the CI/CD pipeline. Training completion is tracked in real time.

This does not eliminate the audit, but it eliminates the weeks of preparation before the audit. When evidence is collected continuously, the audit becomes a review of existing documentation rather than a data-gathering exercise. Learn more about what a SOC 2 readiness assessment looks like when evidence is already in place.

Automating control verification

Forward-looking teams are automating the verification of controls themselves, not just the detection of violations. Examples:

• Automated access reviews that pull current access from identity providers, compare against role requirements, and flag anomalies for human review rather than requiring manual review of every user.
• Policy-as-code that verifies infrastructure configurations against security baselines continuously, not quarterly. We have written about how AWS security misconfigurations persist precisely because verification is periodic rather than continuous.
• Automated evidence packaging that compiles audit-ready documentation from system logs, Git history, and identity providers, reducing the manual effort from weeks to hours.

Using managed compliance services

Rather than building internal compliance capacity from scratch, many organizations are offloading the verification burden to managed services. This model provides:

• Dedicated compliance expertise without the overhead of full-time hires in a tight labor market.
• Established evidence collection workflows that have been tested across hundreds of audits.
• Auditor relationships that reduce friction during the audit engagement.
• Ongoing monitoring rather than periodic check-ins, catching gaps before they become audit exceptions.

This approach is particularly effective for startups and SMBs that need SOC 2 or ISO 27001 certification but cannot justify a full internal compliance team. The economics of the hidden costs of compliance, which we have written about previously, make managed services increasingly attractive as the verification burden grows.

Building security into the development pipeline

Instead of treating security as a gate at the end, these organizations embed verification into the development workflow:

• Automated security tests run in CI/CD, catching common issues (secrets in code, dependency vulnerabilities, misconfigurations) before they reach production.
• Pre-approved patterns for common operations (database access, API authentication, file uploads) reduce the need for per-change security review.
• Self-service security guardrails that let developers make safe choices by default, reducing the volume of findings that require human triage.

The goal is not to eliminate human security review, but to reserve it for decisions that genuinely require human judgment: architecture reviews, threat modeling, complex authorization logic, and risk acceptance decisions.

The Path Forward

The security verification bottleneck is real, but it is not inevitable. Organizations that recognize it as a structural problem rather than a tooling problem can address it systematically.

If you are feeling the squeeze today, here is a practical starting point:

1. Measure your verification backlog. How many scanner findings are awaiting triage? How many days of engineering time does audit preparation consume? How long do security reviews sit in queue? You cannot fix what you do not measure.

2. Identify the highest-volume manual tasks. Evidence collection, access reviews, and change management documentation are typically the biggest time sinks. Start automating there.

3. Evaluate whether your audit model fits your delivery model. If you deploy daily but audit annually, the evidence collection burden will keep growing. Continuous compliance monitoring can flatten the curve.

4. Consider managed compliance for verification-heavy work. Compliance expertise is scarce. Managed services can provide it without the hiring overhead, especially for SOC 2 and ISO 27001 programs where the verification burden is well-defined.

5. Reserve human judgment for what actually requires it. Not every finding needs a senior security engineer. Automate the routine, escalate the complex, and protect your most constrained resource: experienced human reviewers.

The companies that will navigate this transition well are the ones that stop treating security verification as a scaling problem to be solved with headcount, and start treating it as a systems design problem to be solved with better workflows, automation, and strategic use of external expertise.

Sources

• GitHub - Research: Quantifying GitHub Copilot's Impact on Code Quality - Data on developer productivity improvements with AI coding assistants
• McKinsey - Unleashing Developer Productivity with Generative AI - Research on generative AI's impact on software development productivity
• Bain & Company - How Generative AI Is Already Transforming Software Development - Analysis of end-to-end SDLC productivity gains from AI adoption
• CSET Georgetown - Generative AI and Software Quality - Evaluation of code quality and bug prevalence in LLM-generated code
• Verizon - 2025 Data Breach Investigations Report - Analysis of breach trends, vulnerability exploitation, and remediation timelines
• OWASP - Broken Object Level Authorization - API Security Top 10 risk classification for authorization flaws
• ISC2 - 2024 Cybersecurity Workforce Study - Global cybersecurity workforce shortage analysis and projections
• CISA - Cyber Workforce - US government cybersecurity workforce development priorities
• AICPA - Trust Services Criteria - Official SOC 2 control requirements and criteria definitions
• ISO - ISO/IEC 27001 Information Security Management - International standard for information security management systems