MITRE ATLAS, NIST AI RMF, Google SAIF, and AATMF get grouped together as "AI security frameworks," but they do four different jobs. Here is what each one is actually for — by orientation, output, and the question it answers.
They are not competitors. MITRE ATLAS catalogs the AI attacks that have happened. NIST AI RMF governs organizational AI risk. Google SAIF sets a security baseline. AATMF is the operational layer — executable adversarial test procedures you run against a live system. Most teams need a governance framework and an offensive one; pick one from each layer.
| Framework | Orientation | What it gives you | Best for | Output |
|---|---|---|---|---|
| MITRE ATLAS | Threat intelligence | A catalog of observed adversarial-ML tactics, techniques, and real-world case studies, modeled on ATT&CK | Understanding what attackers have already done to AI systems | Knowledge base |
| NIST AI RMF | Governance / risk | A risk-management lifecycle — Govern, Map, Measure, Manage — for trustworthy AI | Managing AI risk at the organizational level | Process framework |
| Google SAIF | Security principles | Core elements and controls for securing AI systems across the lifecycle | Setting a security baseline and shared vocabulary | Principles / guidance |
| AATMF | Operational / offensive | 15 tactics → 240 techniques → 2,152+ executable procedures → 4,980+ adversarial prompts | Actually red-teaming an AI system and producing evidence | Runnable test suite |
MITRE ATLAS (Adversarial Threat Landscape for Artificial-Intelligence Systems) is a curated knowledge base of tactics and techniques adversaries use against machine-learning systems, structured like MITRE ATT&CK and grounded in observed, real-world case studies. Its value is awareness: it tells you which attack classes exist and have been seen in the wild. It is descriptive, not executable — ATLAS does not hand you the test that proves your system is exposed.
The NIST AI Risk Management Framework is a voluntary, organization-level framework for managing the risks of AI through four functions — Govern, Map, Measure, and Manage. It answers "how does our organization handle AI risk responsibly?" It is deliberately framework-agnostic about how you measure; the Measure function explicitly expects you to bring testing and red-teaming methods to the table.
Google's Secure AI Framework (SAIF) is a set of security principles and controls for building and deploying AI safely — extending traditional security fundamentals to AI-specific risks. It is a baseline and a shared language. Like NIST AI RMF, it tells you what good looks like rather than shipping the offensive procedures that test for it.
AATMF (the Adversarial AI Threat Modeling Framework) is the one in this set built to be run. Where the others describe, govern, or set principles, AATMF v3.1 ships a structured, executable taxonomy: 15 tactics, 240 techniques, 2,152+ attack procedures, and 4,980+ adversarial prompts, with crosswalks to NIST AI RMF and MITRE ATLAS. It is the artifact a red-teamer actually uses against a live LLM, agent, or RAG pipeline — and the evidence it produces is what a NIST Measure step or an assurance review consumes. It is open-source (CC BY-SA) and published on ResearchGate.
The cleanest way to read these four is as rungs on one ladder, not as a bracket where one wins:
If you only adopt one, pick the layer your gap is in. If your AI risk is undocumented, start with governance. If you have policy but no proof, start with offensive testing. For a deeper head-to-head on the offensive layer, see AATMF vs MITRE ATLAS and AATMF vs MAESTRO.
It depends on the job. For executable offensive testing of an AI system, AATMF is the operational choice. For organizational risk governance, NIST AI RMF. For threat intelligence on observed attacks, MITRE ATLAS. For a security baseline, Google SAIF. Mature teams pair a governance framework with an offensive one.
No — complementary. ATLAS documents what AI attacks exist; AATMF gives you the procedures and prompts to test for them.
MITRE ATLAS, NIST AI RMF, and AATMF are publicly available at no cost (AATMF under CC BY-SA). Google SAIF is published as guidance by Google. None charge a license fee.