Detection Engineering

4 articles

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Map

The Future of AI Security Engineering: From AppSec to AgentSec to Autonomous SOCs

The future of AI Security Engineering is a platform discipline that extends AppSec into LLM applications, creates AgentSec for autonomous workflows, builds AI-native telemetry for detection and incident response, and turns governance into continuous evidence rather than annual paperwork.

9 min read

Defend

Security Monitoring for AI Agents: How to Detect Dangerous Tool Use Before Damage Happens

Security monitoring for AI agents requires tool-call telemetry, action-sequence detection, approval-state tracking, memory monitoring, credential visibility, anomaly detection, and kill-switch response paths. Dangerous tool use should be detected before it becomes data leakage, unauthorized change, financial impact, or customer-facing error.

10 min read

Defend

AI Incident Response: Playbooks for Prompt Injection, Model Abuse, Data Leakage, and Rogue Agents

Most incident teams already know how to isolate systems and preserve logs. AI changes the shape of the evidence. The response process must include prompts, retrieval context, tool actions, and model versions.

3 min read

Defend

Detection Engineering for AI Systems

Traditional detections miss AI-specific abuse because the action can start in language and end in a side effect. The control gap is not only alert content. It is missing telemetry.

3 min read