From NIST to OWASP: The Frameworks That Matter

How to Keep Your AI Deployments Aligned with the Evolving Risk Landscape

Why frameworks are becoming the New Baseline for AI Risk

In the race to build generative AI (GenAI) systems, one reality stands out: technology moves faster than regulation. Governments are still defining what “safe AI” means, while enterprises deploying these systems already need guardrails now, not years from now.

That’s why frameworks like NIST AI RMF, MITRE ATLAS, OWASP’s Top 10, MAESTRO, ISO 42001, and others (with new ones emerging almost monthly for specific domains), have become the practical backbone of AI assurance, long before any binding regulation takes effect.

When user-generated content platforms faced social and safety crises, the EU eventually introduced the Digital Services Act (DSA), but only after platforms like Facebook and YouTube had already built their own moderation and transparency policies. Similarly, the General Data Protection Regulation (GDPR) codified privacy expectations that companies had been wrestling with for years.

In short, regulation has always trailed innovation, but with AI, that lag has become untenable. The technology is evolving faster than any previous digital transformation, reshaping industries and societies in real time, long before policymakers can respond. While the EU AI Act is advancing, it remains largely focused on classification and transparency, not the operational details that matter most in deployment.

For product and security leaders, that means two things:

1. You need to understand which frameworks define the conversation, because these are what procurement, audit, and regulators will increasingly expect.
2. You need a way to continuously operationalize alignment, as frameworks evolve faster than formal policies.

In the next section, we’ll explore why the risk landscape is expanding so quickly, and how these frameworks are converging into an emerging global standard for AI safety and security.

Key frameworks that are shaping AI risk management

Below are the frameworks you should have on your radar, not because they will cover everything, but because they set the tone and construct the vocabulary for how AI risk is being managed across the industry.

1. NIST AI Risk Management Framework (AI RMF)

• Publisher: U.S. National Institute of Standards and Technology (NIST), a federal agency under the Department of Commerce that develops voluntary, science-based frameworks and standards to improve technology safety and reliability. 
• Released: NIST AI RMF was released in January 2023; the Generative AI Profile (AI-600-1), a more comprehensive, companion resource, was added in July 2024, pursuant to President Biden’s Executive Order (EO) 14110 on Safe, Secure, and Trustworthy Artificial Intelligence (which was later revoked by President Trump).
  Purpose: To help organisations identify, assess, manage, and monitor the unique risks of AI systems and promote trustworthy, responsible AI.
• Why it was created: There was no unified, operational framework for AI risk comparable to what ISO 27001 or SOC 2 offer for cybersecurity and data protection.
• Core Functions:
  Govern – Establish accountability, roles, and culture for AI risk.
  Map – Understand context, intended use, and potential impacts.
  Measure – Evaluate model performance, data quality, and exposure to bias or attack.
  Manage – Prioritise, mitigate, and monitor risks continuously.
• Defines “Trustworthy AI” as: Valid & reliable | Safe, secure & resilient | Accountable | Transparent | Privacy-enhanced | Fair & bias-aware | Explainable & interpretable.
• Adoption: Voluntary but increasingly referenced in enterprise AI governance, vendor assessments, and policy drafts worldwide.
• Relation to regulation: Serves as a non-binding foundation for future AI standards and legislative frameworks, including the U.S. federal guidance ecosystem and EU-aligned assurance schemes.

Key Enterprise Takeaway:

The AI RMF provides a common language that links technical teams, risk managers, and regulators, helping prove AI systems are not only effective, but safe and auditable.

If you don’t yet have a mapping of your AI processes (governance, development, deployment, monitoring) to an AI-specific risk framework, starting with NIST AI RMF gives you a foundation that most stakeholders recognize.

2. OWASP GenAI / LLM Top 10 for Large Language Model Applications

• Publisher: Open Worldwide Application Security Project (OWASP), a global non-profit foundation dedicated to improving software security worldwide. As an open, non-profit initiative, it invites contributions from experts across industries and academia, ensuring the list evolves with real-world attack data and practical defense insights.
• First Released: August 2023 (LLM Top 10 v1.0); expanding in 2025 to include the Agentic AI Security Framework.
• Purpose: To identify and describe the top security and safety vulnerabilities specific to large language models (LLMs) and agentic AI applications.
• Why it was created: Traditional web-application security frameworks couldn’t capture the unique risks of GenAI applications and LLMs.
• Structure: The OWASP LLM Top 10 provides a ranked taxonomy of the most critical vulnerabilities for developers, security engineers, and red teamers to prioritize.
• Community-driven development: OWASP GenAI is built and maintained by a global community of practitioners from security engineering, policy, AI research, and operations. This makes it one of the most responsive and credible living knowledge bases in the GenAI security space.
• Evolution: The forthcoming Agentic Security Framework extends the work to autonomous and tool-using AI agents, defining early standards for safe orchestration and control.
• Adoption: Rapidly emerging as the baseline security checklist for GenAI and agentic AI systems, referenced in enterprise AppSec programs, cloud provider guidelines, and AI assurance reports.

Key Enterprise Takeaway:
OWASP’s open-source model makes it uniquely valuable for enterprises: it transforms cutting-edge research and attack intelligence into actionable, testable controls. Use the OWASP LLM Top 10 as a baseline for your threat modelling or red-team programs. It helps translate “model risk” into engineering playbooks, including the most current, community-verified threat classes.

3. MITRE ATLAS (Adversarial Threat Landscape for AI Systems)

• Publisher: MITRE Corporation, a U.S. federally funded research and development center (FFRDC) that supports national security, cybersecurity, and emerging technology research.
• First Released: 2021; continuously updated with new attack and defense techniques.
• Purpose: To document and categorize real-world adversarial tactics and techniques used against machine learning and AI systems, creating a shared, evidence-based understanding of how these systems can be attacked and defended.
• Why it was created: Security teams needed the AI equivalent of MITRE ATT&CK,  a framework to help them understand, simulate, and mitigate adversarial threats targeting data pipelines, model training, and inference processes.
• Structure:
  • Organized into phases of the AI lifecycle: Data, Training, Deployment, and Maintenance, with each phase containing detailed adversarial techniques (TTPs), and acting as a playbook of attacker-mindset scenarios (which is aimed to be use for red teaming)
  • Includes case studies from real-world incidents and links to defensive mitigations and security research papers.
• Community-driven updates: Maintained by MITRE’s AI Red Team and open contributors from government, academia, and private industry.
• Integration with cybersecurity: ATLAS techniques can be mapped to traditional ATT&CK tactics (e.g., Reconnaissance, Exfiltration, Impact), enabling joint AI and cyber threat modeling with unified SOC visibility.
• Adoption: Used by major technology vendors, national labs, and enterprise red teams to design AI-specific threat models, attack simulations, and defense evaluations.
  

Key Enterprise Takeaway:
MITRE ATLAS bridges the gap between AI safety and cybersecurity. By integrating ATLAS into existing risk and red-team programs, enterprises can quantify exposure to adversarial AI risks, align testing with a globally recognized standard, and communicate AI threat readiness in the same language their security and compliance stakeholders already understand.

4. MAESTRO Framework (Multi-Agent Environment, Security, Threat, Risk, and Outcome)

• Publisher: Cloud Security Alliance (CSA), a nonprofit industry consortium that brings together cloud providers, enterprises, government agencies, and security researchers to develop and promote best practices for secure cloud computing.
• Released: February 2025.
• Purpose: MAESTRO provides a structured, defense-oriented framework for identifying, modeling, and mitigating threats in “agentic AI” systems, those capable of autonomous reasoning, tool use, and multi-agent coordination.
• Why it was created: Existing AI frameworks (like NIST AI RMF or MITRE ATLAS) focused primarily on static models. As agent-based architectures emerged, where AI systems plan, act, and interact, the community needed a new framework to address autonomy-driven risks such as:
  • Goal misalignment and emergent behavior.
  • Prompt-to-action vulnerabilities (unsafe execution of generated instructions).
  • Inter-agent manipulation and coordination exploits.
  • Tool-use abuse through APIs or plug-ins.
• Structure:
  MAESTRO defines six analytical layers that span the agentic AI lifecycle:
  • Foundation Model Layer: inherent LLM or multimodal model vulnerabilities.
  • Data Operations Layer: integrity and governance of training and runtime data.
  • Agent Framework Layer: orchestration, reasoning loops, and planning control.
  • Infrastructure Layer: API gateways, connectors, and execution environments.
  • Observability Layer: telemetry, behavioral monitoring, and audit logging.
  • Ecosystem Layer: interactions across agents, plug-ins, and external services.
• Key principles:
  • Continuous threat modeling, not point-in-time audits.
  • Emphasizes human-in-the-loop governance for agent actions.
• Community and collaboration: Built as an open, community-driven initiative, with input from AI red teams, DevSecOps professionals, and trust & safety experts.
• Adoption: Rapidly gaining traction among cloud providers, enterprise AI security teams, and academic safety labs experimenting with multi-agent systems and autonomous copilots.

Key Enterprise Takeaway:
Agentic AI is the next frontier in enterprise innovation, and teams are racing to deploy agents fast. MAESTRO doesn’t slow that momentum; it zooms in on one of the earliest stages of the roadmap, embedding threat modeling before large-scale rollout. Integrating it alongside policy creation and regulatory review helps ensure agents are launched securely and responsibly from day one.

5.  Governance & InfoSec Standards: ISO 42001 & ISO 27001

ISO / IEC 42001: AI Management System Standard

• Released: December 2023.
• Scope: The first international management system standard specifically for Artificial Intelligence.
• Publisher: International Organization for Standardization (ISO) and the International Electrotechnical Commission (IEC) are independent, international, non-governmental standard-setting bodies that coordinate a network of national standards across 170 countries. 
• Purpose: To help organizations establish, implement, maintain, and continually improve an AI Management System (AIMS), a governance structure that ensures AI is developed and used responsibly, safely, and transparently.
• Key focus areas: AI lifecycle governance, human oversight, accountability, robustness, data quality, and continual improvement.
• Relation to GenAI: ISO 42001’s control set is model-agnostic; it applies equally to predictive, generative, and agentic systems. In practice, enterprises use it to demonstrate governance readiness and responsible-AI maturity for GenAI deployments.
• Adoption: Early uptake among global tech, financial services, and healthcare firms seeking certifiable evidence of AI governance.

ISO / IEC 27001: Information Security Management System (ISMS)

• Most recent update: 2022 revision.
• Scope: The cornerstone global standard for information security management, covering data confidentiality, integrity, and availability across systems and suppliers.
• Purpose: Provides a certifiable management framework for identifying, managing, and mitigating information-security risks.
• Relation to GenAI: Indirect but essential. GenAI systems depend on vast data pipelines, APIs, and model-hosting infrastructure, all of which fall under ISMS controls. ISO 27001 ensures those environments remain secure, audited, and compliant.
• Typical overlap with AI frameworks: Access control, encryption, supply-chain security, and incident-response processes that underpin trustworthy AI operations.

Key Enterprise Takeaway:

While neither framework is GenAI-specific, ISO 42001 and 27001 form the governance and security backbone for any AI deployment. 42001 defines how to manage AI responsibly; 27001 secures the infrastructure it runs on. 

Because ISO and IEC, the organization behind these frameworks  are neutral, global, and industry-driven, their standards tend to be more stable and broadly adopted than national regulations. They reflect multi-stakeholder consensus rather than political directives, which makes them particularly trusted in cross-border enterprise compliance.

What makes today’s AI frameworks so meaningful

What sets these frameworks apart isn’t just what they cover, it’s how they’re built and who builds them.

• They emerge from the field, developed and adopted by practitioners (engineers, researchers, trust & safety teams) who report real risks and design real mitigations.
• They’re created by non-governmental, community-led bodies like NIST, OWASP, and CSA, not by political institutions vulnerable to policy shifts or revocations.
• They evolve online, openly, and fast, often within weeks of new threats or model behaviors appearing.
• They spread bottom-up: adopted by practitioners and enterprises because they work, not enforced by regulators because they must.

This makes these frameworks more practical, more current, and more resilient than traditional regulation. They reflect the realities of deploying GenAI in production, not just the theory of how it should be governed.

From frameworks to practice: Automated compliance mapping in action

Understanding frameworks is one thing. Operationalizing them, across dozens of systems, regions, and use cases, is another. Frameworks like those discussed above (and the many new ones emerging each month for specific risks or use cases), together with an ever-evolving regulatory landscape, create a constantly shifting map of requirements and expectations. It’s simply too much to track manually.
For most enterprises, manually mapping and monitoring compliance across frameworks and jurisdictions isn’t feasible. It demands constant updates, cross-team coordination, and deep technical interpretation that quickly become unsustainable at scale.

That’s why automation is essential.

ActiveFence’s Real-Time Guardrails  and Auto Red Teaming, continuously operationalize AI safety and security policies, integrating the latest framework revisions, regulatory updates, and best practices directly into production.

Compliance becomes live and adaptive, embedded into your applications and agents as they evolve. Every control and policy can be filtered or adjusted by framework, regulation, or internal standard, providing full visibility and traceability as your AI systems grow.

Guardrails Platform interface showing policy categories mapped across OWASP, NIST, MITRE, and MAESTRO frameworks

In practice, this means your organization stays continuously aligned and audit-ready, even as the frameworks and risks themselves change.

See It in Action

👉 Book a demo to see how your organization can automate compliance mapping across AI frameworks to operationalize AI trust at scale.