Comply · Global standards & industry frameworks
MITRE ATLAS
The adversarial-threat knowledge base for AI systems.
MITRE ATLAS (Adversarial Threat Landscape for Artificial-Intelligence Systems) is a knowledge base of real-world adversarial tactics and techniques against machine-learning systems — the AI-focused counterpart to MITRE ATT&CK — used to understand, detect, and defend against attacks on AI.
Who it applies to
Security and ML teams threat-modeling and defending AI/ML systems.
Key obligations
- Map AI systems against ATLAS tactics and techniques
- Detect adversarial activity (evasion, poisoning, extraction)
- Implement mitigations for identified techniques
- Feed incidents back into threat models
How Maetra maps agents to MITRE ATLAS
Maetra's Secure module detects and blocks runtime attacks that map to ATLAS techniques, and Audit records incidents so defenses can be tuned over time.
In practice, Maetra:
- Scans and fingerprints each agent. Discover reads the agent’s code — its tools, data access and sensitivity, actions, model, and environment — into an evidence-backed profile tied to the exact file and commit.
- Decides what applies. That profile determines whether MITRE ATLAS is in scope for the agent and which of its requirements apply.
- Auto-detects controls and surfaces gaps. Controls your code already satisfies are detected automatically from the scan; the rest become a clear list of gaps, each tied to the requirement and the evidence it still needs.
- Proves it and keeps it current. Close gaps with linked evidence or generated documents — reused across every framework the same control supports — and Maetra re-checks on each rescan and flags evidence that has gone stale.
Related frameworks
Prove MITRE ATLAS compliance with Maetra
Classify your AI agents once and Maetra maps them to MITRE ATLAS and every other framework it supports — generating the evidence and documentation, tracking gaps and deadlines, and sealing every decision in an immutable audit trail.