AI Security
What are the security highs and lows of vibe coding?
Vibe coding speeds up prototypes but ships code nobody read. Research shows AI assistants can raise insecure output and false confidence. Add guardrails.
Oct 18, 20265 min read
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Can you use an LLM to triage vulnerability reports safely?
Yes, if you treat report text as untrusted input, limit what the model can do, keep a human on decisions and log everything. A checklist for engineering teams.
Oct 18, 20264 min read
AI Security
How can generative AI help DevSecOps without adding new risk?
Generative AI can speed triage, review and fixes in DevSecOps, but its output is untrusted input. Add human review, least privilege and tests first.
Oct 18, 20264 min read
AI Security
How do you use AI coding assistants safely in an enterprise?
AI coding assistants raise data leakage, insecure code and dependency risks. Set data rules, require review and scan the output with this policy checklist.
Oct 18, 20264 min read
AI Security
What are toxic flows in MCP and how do you break them?
A toxic flow is a chain of tool calls where untrusted input, private data and an outbound channel meet. Map your MCP tools for that mix and cut one leg.
Oct 18, 20264 min read
AI Security
How do you test LLM guardrails with a regression suite?
Treat guardrails like code: keep a versioned set of blocked and allowed cases, run it on every change, and track false blocks too. How to build one.
Oct 18, 20264 min read
AI Security
How to test LLM guardrails with a regression suite
Guardrails drift when prompts, models and rules change. Build a versioned regression suite with expected outcomes and pass thresholds, and run it in CI.
Oct 18, 20264 min read
AI Security
Ten practices for securely developing with AI, from NCSC and OWASP
Treat model output as untrusted input, limit agent permissions, vet models and data, and log everything. Ten practices from NCSC and OWASP LLM guidance.
Oct 18, 20264 min read
AI Security
What are the steps to adopt AI in a cybersecurity program?
Adopt AI in security by setting ownership, picking one use case, securing the AI itself, piloting and measuring. Seven steps built on NIST CSF 2.0.
Oct 18, 20264 min read
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