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AI SAST
Guides on AI static application security testing: how AI SAST cuts false positives, how it compares with DAST, and how to roll it out in CI without slowing developers down.
AI security
Engineering guides on securing AI and LLM applications: the OWASP LLM Top 10 as a working backlog, prompt injection testing in CI, and controls you can ship this sprint.
Secret detection
Guides on finding and rotating leaked credentials: why deleting a line does not remove a secret, how to scan full git history, and the rotation-first incident response order.
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Securing AI & LLM applications: the engineering checklist
A 24-point checklist covering prompt injection, data leakage, model supply chain, output handling and monitoring — mapped to the OWASP Top 10 for LLM Applications.
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AI SAST explained: how AI static analysis cuts false positives
What AI SAST actually changes compared with rule-based static analysis, where the accuracy gains come from, and how to roll it out in a CI pipeline without slowing developers down.
OWASP LLM Top 10: a practical checklist for engineering teams
A working engineer’s pass through the OWASP Top 10 for LLM Applications — what each risk looks like in a real product, and the concrete control you can ship this sprint.
Secrets in git history: finding, rotating and preventing leaks
Why deleting a leaked credential in a new commit does nothing, how to scan full git history safely, and the rotation-first response order that limits damage.
SAST vs DAST: what each one catches, and how to run both
A practical comparison of static and dynamic application security testing — what each method can and cannot see, where they overlap, and how to sequence both in one pipeline.
Prompt injection testing: building an adversarial suite for CI
How to test LLM features for prompt injection automatically — payload corpus design, what to assert, and how to wire the suite into CI so a prompt edit cannot silently remove a control.