Comparing AI Security Frameworks — NIST AI RMF vs Google SAIF vs OWASP Top 10

Responsible use: Use this framework comparison to improve organizational security planning and framework selection on systems you are authorized to assess or manage.

Purpose

Organizations face a confusing landscape of AI security frameworks. NIST AI RMF, Google SAIF, and OWASP Top 10 for LLM Applications all provide valuable guidance — but they answer different questions. This lesson teaches you when to use each framework and how they work together.

The core insight: No single framework covers everything. Effective AI security requires combining frameworks based on your organization's needs.

The three frameworks at a glance

Understanding these distinctions helps you select the right guidance for the right situation.

NIST AI RMF — Governance and lifecycle focus

The NIST AI Risk Management Framework provides a process model for continuous risk management through four functions:

• Govern — Establish policies, roles, and accountability
• Map — Identify systems, use cases, and stakeholders
• Measure — Quantify and evaluate risks
• Manage — Implement controls and monitor effectiveness

When to use NIST AI RMF:

• Your organization needs to establish AI risk management as an ongoing program
• You must satisfy governance, compliance, or regulatory requirements
• You want a flexible, adaptable framework that applies across industries
• You need to integrate AI risk into existing enterprise risk management

Strength: NIST AI RMF provides the governance layer that makes other frameworks actionable. Without governance, technical recommendations lack accountability.

Google SAIF — Implementation and defense focus

Google's Secure AI Framework provides a defense architecture organized around four pillars:

• Expand foundations — Extend security practices to AI infrastructure
• Extend detection — Integrate AI threats into security operations
• Automate defenses — Use continuous testing and evaluation
• Harmonize controls — Maintain consistent security across platforms

When to use Google SAIF:

• Your team is implementing AI security controls and needs prescriptive guidance
• You want to extend existing security operations into the AI domain
• You need a checklist-like structure for organizational readiness
• You are building AI systems and want to embed security from the start

Strength: SAIF translates governance intent into concrete defensive actions. It answers "what should we do?" after NIST AI RMF answers "how should we organize?"

OWASP Top 10 — Technical risk focus

The OWASP Top 10 for LLM Applications provides a risk taxonomy identifying the most critical security risks:

• LLM01: Prompt Injection — Manipulating model behavior through crafted inputs
• LLM02: Insecure Output Handling — Failing to sanitize model outputs
• LLM03: Training Data Poisoning — Corrupting training data
• And seven additional technical risks...

When to use OWASP Top 10:

• You need to prioritize technical security risks for LLM applications
• You are conducting threat modeling or security reviews
• You want industry-standard risk language for communicating with stakeholders
• You need specific mitigation guidance for identified vulnerabilities

Strength: OWASP provides the technical risk vocabulary that SAIF defenses address and NIST AI RMF governs. It defines the problem space.

When to use which framework — Decision guide

Use this decision framework to select guidance:

Starting from scratch?

→ Begin with NIST AI RMF to establish governance and risk management processes.

Have governance but need implementation guidance?

→ Add Google SAIF for prescriptive defense architecture.

Building or securing specific LLM applications?

→ Reference OWASP Top 10 for technical risk prioritization.

Responding to a security incident?

→ Use SAIF for detection and response, guided by OWASP for risk classification, within your NIST AI RMF process.

Communicating with executives or boards?

→ Lead with NIST AI RMF (governance language) and reference OWASP (industry standard).

Planning security controls for a new AI system?

→ Combine SAIF (what to implement) with OWASP (what risks to address).

How frameworks complement each other

These frameworks work together in layers:

┌─────────────────────────────────────┐
│  NIST AI RMF — Governance Layer     │
│  "How we manage risk"               │
├─────────────────────────────────────┤
│  Google SAIF — Defense Layer        │
│  "What we implement"                │
├─────────────────────────────────────┤
│  OWASP Top 10 — Risk Layer          │
│  "What we defend against"           │
└─────────────────────────────────────┘

Example integration: An organization uses NIST AI RMF to establish quarterly risk reviews (Govern → Map → Measure → Manage). During the "Measure" phase, they reference OWASP Top 10 to identify which LLM risks need evaluation. Based on findings, they implement SAIF's "Automate defenses" pillar to add continuous adversarial testing.

Without NIST AI RMF, SAIF implementation lacks accountability. Without SAIF, NIST AI RMF remains theoretical. Without OWASP, both lack specific risk targets.

Real-world application — Multi-framework scenario

Consider a healthcare organization deploying an AI assistant for patient scheduling:

NIST AI RMF application:

• Govern: Policy requiring risk assessment for all patient-facing AI
• Map: System processes PHI, schedules appointments, sends reminders
• Measure: Quarterly red-teaming identifies prompt injection vulnerabilities
• Manage: Implemented confirmation gates for appointment changes

Google SAIF application:

• Expand foundations: Input validation on all user queries
• Extend detection: Monitoring for adversarial prompt patterns
• Automate defenses: Continuous adversarial testing in CI/CD
• Harmonize controls: Same security standards across dev and prod

OWASP Top 10 application:

• LLM01 (Prompt Injection): Primary concern for scheduling manipulation
• LLM02 (Insecure Output Handling): Sanitizing responses that include PHI
• LLM06 (Sensitive Information Disclosure): Preventing leakage of patient data

The result: Governance (NIST) drives implementation (SAIF) to address specific risks (OWASP).

Failure modes — Common framework misuse

Defender takeaways

1. Match framework to need — NIST for governance, SAIF for implementation, OWASP for technical risks
2. Combine frameworks — Effective security uses all three layers together
3. Start with governance — NIST AI RMF provides the foundation that makes other frameworks sustainable
4. Add implementation next — SAIF turns governance into concrete defenses
5. Reference risks throughout — OWASP keeps efforts focused on real threats
6. Review framework selection annually — Organizational maturity changes; frameworks should adapt

Related lessons

• NIST AI RMF: The Four Functions of AI Risk Management — Deep dive into governance lifecycle
• The SAIF Framework — Four Pillars of AI Security — Prescriptive defense guidance
• OWASP Top 10: Prompt Injection in Context — Technical risk taxonomy
• Prompt Injection as Initial Access — Attack-chain context for framework planning

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Framework references: NIST AI Risk Management Framework (NIST AI 100-1), Google Secure AI Framework (SAIF), OWASP Top 10 for LLM Applications (2025). All trademarks belong to their respective owners. This lesson provides educational interpretation of publicly available materials.