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AI Security Frameworks

Introduction

As AI systems move from research prototypes into production infrastructure, securing them has become a first-order concern. Traditional security playbooks don’t fully account for the unique attack surfaces AI introduces: poisoned training data, prompt injection, model exfiltration, adversarial manipulation, and the emergent unpredictability of probabilistic outputs.

The good news is that several major standards bodies, industry groups, and open-source communities have published frameworks to address these risks. The bad news is that the sheer number of overlapping frameworks can create what many CISOs describe as compliance chaos.

This post surveys the most prominent AI security frameworks available today, describes what each one brings to the table, and offers practical guidance on how they complement each other.

1. NIST AI Risk Management Framework (AI RMF)

Published by: U.S. National Institute of Standards and Technology
Type: Governance and risk management
Scope: Broad — covers the full AI lifecycle at an organizational level

The NIST AI RMF is a voluntary framework designed to help organizations identify, assess, and mitigate risks unique to AI systems. It goes beyond traditional vulnerability management by addressing model behavior — the fact that probabilistic systems can produce wildly different outputs from the same input, potentially revealing sensitive data or affecting downstream systems.

The framework is built around four core functions: Govern, Map, Measure, and Manage. These provide a structured process for embedding AI risk management into enterprise governance rather than treating it as an afterthought.

In 2025, NIST released a Cybersecurity Framework Profile for AI that allows organizations already using the NIST CSF to integrate AI risk management into their existing security posture. NIST is also developing Control Overlays for Securing AI Systems, with a public draft expected in early 2026.

Best for: Executives, risk managers, and organizations that need a top-down governance structure for AI risk.

2. OWASP Top 10 for LLM Applications (2025)

Published by: Open Worldwide Application Security Project (OWASP)
Type: Developer-focused vulnerability taxonomy
Scope: Large language models and generative AI applications

OWASP adapted its well-known Top 10 methodology to identify the most critical security vulnerabilities in LLM-powered applications. The 2025 edition covers:

  1. LLM01 — Prompt Injection: Crafted inputs that override model instructions.
  2. LLM02 — Sensitive Information Disclosure: Models leaking PII, credentials, or confidential data.
  3. LLM03 — Supply Chain Vulnerabilities: Compromised models, datasets, or dependencies.
  4. LLM04 — Data and Model Poisoning: Tampered training data introducing backdoors or biases.
  5. LLM05 — Improper Output Handling: Failing to sanitize model outputs before downstream use.
  6. LLM06 — Excessive Agency: Granting LLMs unchecked autonomy to take real-world actions.
  7. LLM07 — System Prompt Leakage: Exposure of internal instructions, API keys, or business logic.
  8. LLM08 — Vector and Embedding Weaknesses: Exploitable flaws in RAG pipelines and vector databases.
  9. LLM09 — Misinformation: Models generating plausible but false content.
  10. LLM10 — Unbounded Consumption: Resource-exhaustion attacks against inference endpoints.

OWASP is also expanding coverage through its Agentic Security Initiative and Multi-Agent System (MAS) threat modeling efforts, addressing the emerging risks of autonomous AI agents.

Best for: Developers and application security teams building or integrating LLM-powered systems.

3. MITRE ATLAS

Published by: MITRE Corporation
Type: Adversary-centric threat knowledge base
Scope: AI and machine learning systems broadly

MITRE ATLAS (Adversarial Threat Landscape for Artificial-Intelligence Systems) is an ATT&CK-style matrix that catalogs real-world adversarial tactics, techniques, and procedures (TTPs) used against AI systems. As of October 2025, it documents 15 tactics, 66 techniques, and 46 sub-techniques, drawn from red-team exercises, academic research, and incident reports.

The October 2025 update added 14 new agentic AI techniques developed in collaboration with Zenity Labs, covering risks specific to autonomous AI agents such as prompt injection, memory manipulation, and tool misuse.

Key resources included with ATLAS:

  • ATLAS Navigator — web-based matrix visualization for threat modeling and coverage mapping.
  • Arsenal — a CALDERA plugin for automated AI red teaming (developed with Microsoft).
  • AI Incident Sharing Initiative — community threat intelligence through anonymized incident reports.
  • AI Risk Database — searchable incident and vulnerability records.

An important practical detail: approximately 70% of ATLAS mitigations map to existing security controls, making integration with current SOC workflows realistic rather than requiring a ground-up rebuild.

Best for: Red teams, security operations, and threat intelligence analysts who need to model adversarial behavior against AI systems.

4. Google Secure AI Framework (SAIF)

Published by: Google
Type: Industry framework for secure AI development
Scope: AI supply chain and deployment lifecycle

Google’s SAIF formalizes the expanded attack surface that comes with modern AI systems. It places secure AI supply chain as a first-class concern alongside detection and response — recognizing that the supply chain now extends well beyond source code to include public datasets scraped from the internet, pre-trained foundation models from open repositories, and third-party orchestration tools.

The framework is organized around six core elements:

  1. Expand strong security foundations to the AI ecosystem.
  2. Extend detection and response to bring AI into an organization’s threat universe.
  3. Automate defenses to keep pace with existing and new threats.
  4. Harmonize platform-level controls to ensure consistent security across the organization.
  5. Adapt controls to adjust mitigations and create faster feedback loops.
  6. Contextualize AI system risks in surrounding business processes.

Best for: Organizations that need a practical, industry-informed approach to securing AI infrastructure and supply chains.

5. ISO/IEC 42001

Published by: International Organization for Standardization / International Electrotechnical Commission
Type: Certifiable international standard
Scope: AI management systems (AIMS)

Released in 2023, ISO/IEC 42001 is the first international standard that organizations can formally certify against for AI management systems. Unlike voluntary frameworks, certification provides third-party verification of compliance — concrete proof for regulators, customers, and stakeholders.

The standard covers risk assessment, transparency, accountability, and the responsible management of AI systems throughout their lifecycle. It is part of a broader set of over 40 AI-related standards under active development by ISO’s SC 42 subcommittee, addressing areas like data quality, model governance, and bias.

ISO/IEC 42001 complements technical frameworks like OWASP and ATLAS by providing the organizational management structure within which those technical controls operate.

Best for: Organizations that need demonstrable, certifiable compliance — particularly those operating in regulated industries or serving enterprise customers.

6. CSA AI Controls Matrix (AICM)

Published by: Cloud Security Alliance
Type: Vendor-neutral control framework
Scope: Cloud-based and generative AI systems

Launched in July 2025 and updated in October 2025, the AICM is the most granular of the major frameworks, providing 243 control objectives across 18 security domains. It covers the entire AI lifecycle — from data pipelines and training environments to model deployment and third-party integrations.

The controls are organized under five pillars:

  • Control Type
  • Applicability & Ownership
  • Architectural Relevance
  • LLM Lifecycle Relevance
  • Threat Category

These domains span traditional information security areas (Identity & Access Management, Incident Response, Data Privacy) alongside AI-specific concerns like Model Security, Data Lineage, Bias Monitoring, and Supply Chain Risk Management.

CSA has also released MAESTRO, a companion framework focused specifically on the orchestration and autonomy risks introduced by agentic AI systems — an area where other frameworks are only beginning to provide coverage.

Best for: Cloud-centric organizations that need detailed, actionable control mappings for AI workloads.

7. EU AI Act

Published by: European Union
Type: Binding regulation
Scope: Any AI system offered or used in the EU market

The EU AI Act is the world’s first comprehensive AI regulation. It classifies AI systems into risk tiers and imposes obligations accordingly:

  • Unacceptable risk — banned outright (social scoring, certain biometric systems).
  • High risk — subject to conformity assessments, transparency requirements, human oversight, and detailed technical documentation.
  • Limited risk — transparency obligations (e.g., disclosing AI-generated content).
  • Minimal risk — no specific obligations beyond voluntary codes of practice.

Unlike the voluntary frameworks above, the EU AI Act carries legal enforcement and significant penalties for non-compliance. Organizations worldwide must comply if they deploy AI systems that affect individuals within the EU.

Best for: Any organization deploying AI systems in or serving the European market, and legal/compliance teams assessing regulatory exposure.

8. SANS Critical AI Security Guidelines

Published by: SANS Institute
Type: Practitioner-oriented security guidelines
Scope: Enterprise AI deployment

The SANS guidelines take a risk-based approach to enterprise AI security, covering access controls, data protection, inference monitoring, governance, and compliance. A key recommendation is incremental deployment — deploying AI in non-critical environments first, then expanding as security controls mature and are validated.

The guidelines also emphasize maintaining an AI Bill of Materials (AIBOM) to document supply chain dependencies, using model registries for lifecycle tracking, and aligning with NIST AI RMF and MITRE ATLAS as complementary reference points.

Best for: Security practitioners and SOC teams looking for hands-on implementation guidance.

How the frameworks fit together

No single framework covers every angle. The most effective approach combines several based on their complementary strengths:

Framework Primary Focus Audience
NIST AI RMF Governance & risk management Executives, risk managers
OWASP Top 10 for LLMs Application vulnerabilities Developers, AppSec teams
MITRE ATLAS Adversarial tactics & red teaming Red teams, SOC analysts
Google SAIF AI supply chain & infrastructure Platform/infra teams
ISO/IEC 42001 Certifiable management system Compliance, legal
CSA AICM Granular cloud-AI controls Cloud security teams
EU AI Act Legal compliance Legal, compliance, leadership
SANS Guidelines Practitioner implementation Security operations

A practical workflow might look like this:

  1. Build LLM applications with OWASP’s vulnerability checklist in mind.
  2. Test them using adversarial scenarios modeled from MITRE ATLAS.
  3. Govern AI risk across the organization using NIST AI RMF or ISO 42001.
  4. Implement detailed controls using CSA AICM, particularly for cloud workloads.
  5. Comply with the EU AI Act and other jurisdiction-specific regulations.

Conclusion

The AI security landscape is maturing rapidly. Two years ago, most organizations had no AI-specific security controls at all. Today, there are robust frameworks spanning governance, development, adversarial testing, and regulatory compliance.

The challenge has shifted from “what do we do?” to “how do we bring coherence to the many options available?” The answer lies in understanding each framework’s strengths and layering them intentionally — using governance frameworks for organizational structure, developer-focused tools for application security, adversarial knowledge bases for threat modeling, and regulatory frameworks for compliance.

Start with the frameworks most relevant to your organization’s risk profile and regulatory environment, and expand from there. The key is to begin — AI-specific threats are not waiting for anyone’s security program to catch up.

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