What are the five core scanning engines in DefenseClaw?

DefenseClaw comprises five specialized scanning engines: 1) Skill Scanner — performs comprehensive security audits on agent skills before they're loaded, including permission analysis, data flow tracking, and dependency chain inspection; 2) MCP Scanner — targets Model Context Protocol servers to detect prompt injection and unauthorized access; 3) A2A Scanner — monitors Agent-to-Agent communications in multi-agent systems; 4) CodeGuard — performs real-time static analysis on agent-generated code; 5) AI BOM Generator — creates complete component inventories for each AI agent, similar to software SBOMs.

How does DefenseClaw's runtime threat detection differ from static scanning?

Static scanning performs one-time security audits before skills/code are loaded, while runtime threat detection continuously monitors every message flowing in and out of AI agents during execution. This means even if a skill passes initial scanning, DefenseClaw can detect and block anomalous runtime behavior such as data exfiltration. Runtime detection includes real-time message inspection, behavioral baseline comparison, ML-based anomaly pattern recognition, and automated response mechanisms.

What does Cisco mean by 'walls, not suggestions' in DefenseClaw's policy enforcement?

Cisco uses 'walls, not suggestions' to distinguish DefenseClaw from AI safety tools that merely issue advisory warnings. DefenseClaw's policy enforcement is hard — violations are actively blocked rather than just logged. This includes block/allow list management, automatic permission revocation when skills are deemed unsafe, network isolation policies restricting agent communications, and data classification-based access controls. Policy violations result in immediate action such as skill removal or sandbox permission revocation.

— AI DAILY

Sources

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