Test against the documented AI-specific vulnerability patterns: injection flaws from training data bias, hardcoded credentials, unsafe deserialisation (e.g., Python pickle), broken access controls, unchecked file operations, buffer overflows in generated C/C++. This is not generic SAST—it’s calibrated to the specific defect taxonomy of AI-generated code.

AI-generated code often produces correct-looking logic that fails on edge cases. LLMs are probabilistic token predictors—they don’t “understand” recursion, halting conditions, or mathematical guarantees. Test specifically for: off-by-one errors in loops, incorrect recursion base cases, race conditions in concurrent code, and silent data corruption.

AI-generated code accumulates technical debt differently than human code: 4x more duplication, divergent implementations of the same concept, stale code paths never cleaned up. Assess architectural coherence: are there multiple implementations of the same function? Orphaned code? Inconsistent error handling patterns across modules?

AI generates code that is syntactically correct but may not reflect the actual business requirement. Human testers with domain knowledge validate that the code does what the business needs—not just what the prompt asked for. This is the layer that no tool can replicate.

AI-generated frontends often pass automated accessibility checks but fail real-world usability. Human testers evaluate screen reader compatibility, keyboard navigation, cognitive load, and edge-case user journeys that automated tools miss.

For regulated industries (healthcare/HIPAA, finance/PCI-DSS, data privacy/GDPR): human experts verify that AI-generated code meets compliance requirements. AI tools trained on public code may not understand industry-specific regulatory constraints.

Set up automated monitoring that tracks AI-generated code metrics over time: defect density per AI tool, vulnerability introduction rate, code churn patterns. This creates a longitudinal dataset that improves testing precision with every release cycle.

AI-generated code has specific performance anti-patterns: unnecessary memory allocation, redundant API calls, inefficient data structures chosen for readability over performance. Profile specifically for these patterns.

Deploy BugBot and AI-augmented test suites that adapt to code changes. But—critically—flag self-healed tests for human review rather than silently accepting them. Self-healing without oversight compounds the vibe coding problem.

Deliver a client-specific playbook: which AI tools produce the best code for their stack, which modules should never be AI-generated (auth, payment, compliance), prompt engineering guidelines for better code output, and code review checklists calibrated to AI defect patterns.

Train the client’s developers to be effective AI code reviewers. Most developers don’t know what to look for in AI-generated code because the failure modes are different from human code. This training becomes a recurring revenue stream.

Help clients establish AI code governance: which code can be AI-generated, what review process applies, what testing standards must be met, and how incidents are tracked back to AI tools.

Monthly or quarterly assessments as the client’s AI code percentage grows. The AI Code Risk Score is tracked over time. New AI tools adopted by the client’s team are evaluated for defect patterns.

When production incidents are traced to AI-generated code, the Pod provides root cause analysis, remediation, and updated testing protocols. This is the “insurance policy” model—clients pay for ongoing protection.