A security context file is a structured document compiling an organisation’s technical security rules in a form an AI coding agent can act on, loaded into every AI coding session before any code is written. As described in The VibeSec Reckoning, what distinguishes it from a casual prompt telling the AI to be secure is operational discipline: the file is versioned, loaded by default, reviewed, and paired with automated checks. It contains non-negotiable rules meant to make the agent refuse requests that violate policy — for example bypassing a check, disabling logging, or setting something to public access — and explain why.
Usage
The article’s authors built one after two near-miss incidents in which an AI suggested insecure configurations while their team was scaling a vibe-coded prototype. Theirs covers zero trust enforcement, secrets management, harness engineering and supply chain integrity; the coverage they recommend for any such file adds AI accountability:
- Zero trust and least privilege — strict identity verification and minimum access rights on every service account and storage resource.
- Secrets management — the AI refuses to generate or store API keys, passwords or tokens in code and routes them to environment variables or a secrets manager.
- Harness engineering gates — SAST scanning, credential scanning and infrastructure validation must pass before deployment, with no reliance on prompt instructions alone.
- Supply chain integrity — only well-established libraries, with regular audits of every dependency for known vulnerabilities.
- AI accountability — all AI-generated code is flagged for peer review and automated security scanning before deployment.
In practice the article suggests loading it as “Rules” in tools such as Claude, Cursor or Replit, and later investing in a shared default layer across all tools.
When It Applies
The practice applies wherever AI agents generate code — including prototypes built by non-technical citizen builders, which the article argues must still meet enterprise security standards. It assumes the organisation has security requirements it can work through and restate in a form the AI can act on. In the Guides and sensors terms the article borrows from harness engineering, the file is an inferential guide: it tells the agent what good looks like but cannot by itself confirm the output is acceptable, so it misfires when relied on alone — prompts can be overridden, misunderstood or ignored, and the deterministic checks and deployment gates must still catch an issue if the agent fails to follow the file. Its standing is one team’s report: the practice is proposed by the authors of that article on the basis of their own incidents and the platform they subsequently rolled out.