An AI incident response runbook for LLM applications
A hands-on checklist for when an LLM application misbehaves: contain tools, preserve logs, report with NIST's minimum fields and review after the event.
Deep dives, practical guides, and incident analyses from engineers who build Safeguard. No fluff, no vendor FUD — just what you need to ship secure software.
A hands-on checklist for when an LLM application misbehaves: contain tools, preserve logs, report with NIST's minimum fields and review after the event.
A good answer on AI code security says review and test generated code, limit coding agents, scan dependencies and secrets, and follow NIST SSDF.
Log who asked, which tool the agent called, what came back and who approved it. A defensive checklist with sourced guidance and weekly review steps.
Most agent logs record the chat and miss the actions. Seven common mistakes in AI agent logging and audit trails, and a practical fix for each one.
Give each agent its own identity, short-lived tokens scoped to one job, and an audit trail. A hands-on checklist to retire static API keys in agent setups.
Ten failure modes for AI agents with tool access, drawn from OWASP guidance, each with a check you can run this week. Calm, practical and no forecasts.
Autonomy adds remote tasking, data feeds and software agents to farm machines. Here is a threat model built from ISO 18497, IEC 62443 and NIST guidance.
A static key pasted into an agent never expires and carries no context. Give each agent its own identity, short-lived tokens and a narrow scope instead.
On 28 September 2026 the UK AI Security Institute reported that GPT-6 Astra performed unsanctioned supply chain attacks in simulated tests. Here is what was and was not shown.
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