By 2026, AI companies rarely rely on a single framework. Instead they assemble a stack of governance, threat-modeling, development, and operational security frameworks to manage the AI lifecycle.
Below is a critical comparison of the 10 most influential frameworks shaping AI security, AI governance, and MLSecOps today.
The 10 AI Frameworks
| Category | Framework | Core Purpose |
|---|---|---|
| Governance | NIST AI RMF | Enterprise AI risk management |
| Governance | ISO/IEC 42001 | AI management system standard |
| Threat modeling | MITRE ATLAS | AI adversary techniques & attack taxonomy |
| Application security | OWASP Top 10 for LLM Apps | GenAI application vulnerabilities |
| ML security | OWASP ML Security Top 10 | Core ML model attack risks |
| Cloud AI security | CSA AI Controls Matrix | Operational security controls |
| Secure AI architecture | Google SAIF | Secure AI system design |
| MLSecOps | Databricks AI Security Framework | Lifecycle security for AI pipelines |
| Agentic AI security | OWASP Agentic AI Top 10 | Risks from autonomous AI agents |
| Threat modeling (agentic systems) | OWASP MAESTRO | Multi-agent threat modeling |
1. NIST AI Risk Management Framework (AI RMF)
Type: Governance and risk management
Developed by the National Institute of Standards and Technology
Core functions:
- Govern
- Map
- Measure
- Manage
It aims to help organizations build “trustworthy AI” programs across the entire lifecycle. (Akto)
Strengths
- Widely accepted by regulators and governments
- Aligns AI risk with enterprise risk governance
- Provides a shared vocabulary for policy and compliance
Weaknesses
- Too abstract for engineers
- Provides few concrete security controls
- Needs complementary frameworks for threat modeling or implementation
Criticism
AI RMF is excellent governance but poor engineering guidance. Many companies treat it as a policy document rather than an operational framework.
2. ISO/IEC 42001
Type: AI management system standard
ISO 42001 defines requirements for AI management systems (AIMS) including risk assessment, governance, and continuous improvement. (Medium)
Strengths
- First certifiable international AI governance standard
- Integrates well with ISO 27001 and ISO 27701
- Helps organizations demonstrate responsible AI practices
Weaknesses
- Heavy compliance focus
- Implementation overhead
- Limited technical security guidance
Criticism
Many critics see ISO 42001 as ISO 27001 applied to AI governance, which helps compliance but does little to address real AI attack vectors.
3. MITRE ATLAS
Type: Threat modeling / adversary knowledge base
Created by the MITRE Corporation
ATLAS catalogues AI attack tactics and techniques, similar to MITRE ATT&CK but focused on ML systems. (Akto)
Examples of attack categories:
- data poisoning
- model extraction
- adversarial examples
- inference manipulation
Strengths
- Best framework for AI red teaming
- Provides structured attacker playbooks
- Maps attack tactics to defensive techniques
Weaknesses
- Not a governance framework
- Does not define operational controls
Criticism
ATLAS answers “how attackers break AI” but not “how organizations secure AI systems.”
4. OWASP Top 10 for LLM Applications
Produced by the OWASP Foundation
Focuses on vulnerabilities unique to generative AI.
Typical risks include:
- prompt injection
- insecure plugins
- model theft
- data leakage
- supply-chain vulnerabilities
Strengths
- Practical checklist for developers
- Clear categories of LLM threats
- Widely adopted by AI application teams
Weaknesses
- Focused on application layer
- Limited coverage of ML pipelines or training infrastructure
Criticism
The framework is GenAI-centric and struggles to cover broader AI architectures such as reinforcement learning systems or multimodal pipelines.
5. OWASP Machine Learning Security Top 10
Focuses on ML-specific attack techniques.
Examples include:
- data poisoning
- model inversion
- membership inference
- model theft. (OWASP)
Strengths
- Strong coverage of ML attacks
- Useful for research and red teaming
Weaknesses
- Draft/rapidly evolving
- Less widely adopted than the LLM Top 10
Criticism
The framework reflects academic ML threats more than real-world production systems, so some organizations find it too theoretical.
6. CSA AI Controls Matrix
Created by the Cloud Security Alliance
The framework defines 243 control objectives across 18 AI security domains, covering data pipelines, model deployment, and third-party risk. (cycoresecure.com)
Strengths
- Very detailed control framework
- Focus on cloud-native AI systems
- Good mapping to compliance frameworks
Weaknesses
- Complex and heavy
- Difficult to operationalize
Criticism
CSA AICM suffers from “control explosion.” Many organizations struggle to prioritize which controls actually reduce risk.
7. Google Secure AI Framework (SAIF)
Created by Google.
SAIF adapts security principles like secure-by-design to AI systems.
Core ideas include:
- protecting training data
- securing model artifacts
- safeguarding inference pipelines
- monitoring runtime behavior
Strengths
- Practical engineering guidance
- Designed for production AI systems
- Integrates with cloud security practices
Weaknesses
- Ecosystem bias toward Google infrastructure
- Less formal than standards frameworks
Criticism
Some security experts view SAIF as “cloud vendor architecture guidance” rather than a neutral industry standard.
8. Databricks AI Security Framework (DASF)
Developed by Databricks.
It maps 62 AI risks across the ML lifecycle including:
- data ingestion
- model training
- inference
- platform operations. (Medium)
Strengths
- Lifecycle-oriented
- Strong MLSecOps focus
- Practical for enterprise AI pipelines
Weaknesses
- Tooling ecosystem bias
- Less recognition outside ML engineering circles
Criticism
DASF is useful operationally but lacks regulatory authority, so it rarely drives governance decisions.
9. OWASP Agentic AI Top 10
Addresses the emerging risks of autonomous AI agents.
Key risks include:
- agent goal hijacking
- tool misuse
- privilege abuse
- autonomous data exfiltration. (Cert Empire)
Strengths
- First structured framework for agentic AI threats
- Reflects real-world autonomous systems
Weaknesses
- Very new
- Limited tooling support
Criticism
The framework is ahead of current adoption. Many organizations are not yet operating fully autonomous AI systems.
10. OWASP MAESTRO
A threat-modeling framework for multi-agent AI ecosystems. (straiker.ai)
Focus areas:
- agent orchestration
- tool access
- multi-agent trust boundaries
- delegation chains
Strengths
- Addresses multi-agent systems
- Useful for agentic architectures
Weaknesses
- Complex methodology
- Limited adoption
Criticism
MAESTRO is powerful but too complex for most teams, making it more common in research and large enterprises than startups.
The Real Problem: Fragmentation
The biggest issue with AI security frameworks today is fragmentation.
Each framework covers one slice of the AI stack:
| Layer | Frameworks |
|---|---|
| Governance | NIST AI RMF, ISO 42001 |
| Threat modeling | MITRE ATLAS |
| App security | OWASP LLM Top 10 |
| ML model security | OWASP ML Top 10 |
| Controls | CSA AI Controls Matrix |
| Architecture | Google SAIF |
| MLSecOps | Databricks DASF |
| Agentic AI | OWASP Agentic Top 10, MAESTRO |
This fragmentation forces organizations to build “framework stacks.”
Example enterprise stack:
- ISO 42001 → governance
- NIST AI RMF → risk program
- MITRE ATLAS → threat modeling
- OWASP LLM Top 10 → application security
- CSA AICM → operational controls
The Deeper Critique: AI Security Is Still Immature
Despite the number of frameworks, the field has several weaknesses:
1. Most frameworks lag real AI attacks
Attack techniques evolve faster than standards bodies.
2. Governance dominates over engineering
Many frameworks focus on policy rather than code security.
3. Agentic AI is barely covered
Autonomous AI introduces entirely new attack surfaces.
4. Lack of unified lifecycle frameworks
Traditional cybersecurity assumes static software, not continuously learning systems.
The Emerging Direction (2026–2030)
Security researchers are moving toward AI-native security models combining:
- continuous evaluation (TEVV)
- adversarial testing
- MLSecOps pipelines
- runtime monitoring
The future likely converges into three unified layers:
- AI governance (NIST / ISO)
- AI threat intelligence (MITRE ATLAS)
- AI operational security (MLSecOps frameworks)
✅ In short:
The most effective AI companies in 2026 do not adopt one framework—they compose several to cover governance, engineering, and operational risk.
