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Speed up reviews, tests, and migrations without letting an unchecked agent drop databases or leak IP.
Code copilots now autocomplete entire services, explain legacy payment flows, and even run toolchains. Without strong scopes they hallucinate packages, cite old APIs, or execute destructive commands while believing they are helpful.
Typical deployments
A Communications of the ACM study found more than a third of Copilot’s outputs contained CWEs, and engineers keep catching copilots inventing non-existent packages or reviving deprecated APIs in mockable stacks.
Wrap IDEs, CLIs, and DevOps agents with command allowlists, human-in-the-loop approvals, and automated lint/SAST/test hooks so dangerous suggestions never reach git or production without review.
Stress-test prompts and tools with jailbreaks, bogus dependencies, and social-engineered tasks to expose how the copilot behaves under pressure—and fix it before engineers rely on it.
Maintain a real-time inventory of models, embeddings, and knowledge bases feeding the copilot, enforce on-prem or VPC deployments, and run license/PII scans so nothing leaves your tenancy.
Control
OWASP SAMM, NIST SSDF, and IEC 62304-style secure SDLC controls that require approvals, testing evidence, and separation of duties for AI-authored code.
Control
Software composition, copyright, and export controls demanding you prove copilots are not copying GPL or customer IP into deliverables.
Control
SOX and internal audit requirements to show who approved automated changes, what data sources the AI used, and how risky commands were gated.
Control
GDPR/CCPA rules on log redaction and masking when developers include real customer data or production logs in prompts.