AEGIS framework maps 39 AI controls across 5 regulatory standards

Artificial intelligence governance has become a labyrinthine challenge for technology leaders. As AI systems proliferate across enterprises, security and risk professionals face an overwhelming array of compliance frameworks, each with distinct requirements and overlapping controls. Enter Forrester’s AEGIS Framework—a comprehensive approach that promises to cut through this regulatory complexity by creating a unified blueprint for AI governance.

AEGIS (AI Enterprise Governance, Intelligence, and Security) represents more than just another acronym for chief information security officers to juggle. This framework addresses a critical pain point: the need to navigate multiple AI governance standards simultaneously while building trustworthy AI systems, particularly the emerging category of AI agents that can act autonomously on behalf of organizations.

The framework’s newly released regulatory mapping template cross-references 39 substantive controls against five major AI governance standards, creating what amounts to a Rosetta Stone for AI compliance. For CISOs, chief information officers, and chief technology officers wrestling with AI governance complexity, AEGIS offers a structured pathway to regulatory alignment.

The mathematics of AI governance complexity

The challenge facing AI governance professionals becomes clear when examining the numbers. Of AEGIS’s 39 controls, 80% map to four or more major frameworks, while 15 controls align with all five major standards: NIST AI Risk Management Framework, EU AI Act, OWASP Top 10 for Large Language Models, MITRE ATLAS, and ISO/IEC 42001:2023.

This overlap reveals both the convergence and fragmentation in AI governance. While frameworks often use similar terminology, their contextual requirements can differ significantly, creating implementation challenges for security teams attempting to satisfy multiple standards simultaneously.

Universal foundations emerge from the complexity

Two frameworks stand out as foundational: every single AEGIS control references both the National Institute of Standards and Technology’s AI Risk Management Framework and ISO/IEC 42001:2023, the international standard for AI management systems. This universal coverage suggests these frameworks provide the essential scaffolding for AI governance programs.

NIST’s framework, developed by the U.S. Department of Commerce, focuses on identifying, assessing, and managing AI risks throughout system lifecycles. ISO/IEC 42001, meanwhile, establishes requirements for AI management systems within organizations. Together, they create a comprehensive foundation that addresses both risk management and operational governance.

The secondary tier includes OWASP’s framework, which appears in 34 controls and addresses specific security vulnerabilities in large language models like ChatGPT or Claude. The EU AI Act, covering 29 controls, brings regulatory teeth to AI governance with legally binding requirements for high-risk AI systems. MITRE ATLAS, mapping to 21 controls, provides a catalog of adversarial techniques that attackers might use against AI systems.

Framework density reveals operational burden

Framework density—the total number of distinct references each standard contributes—serves as a proxy for operational complexity. The EU AI Act leads with 80 unique references, reflecting its comprehensive regulatory scope covering transparency requirements, human oversight mandates, and lifecycle risk management. This density translates to significant operational demands for organizations subject to European regulations.

NIST contributes 49 references, anchoring risk management and system monitoring requirements. OWASP adds 41 references focused on LLM-specific threats like prompt injection attacks, where malicious users manipulate AI system inputs to produce unintended outputs, and data leakage vulnerabilities. MITRE ATLAS maps to 20 controls, cataloging adversarial techniques and corresponding mitigations.

Understanding these density patterns helps security leaders forecast resource allocation needs and prioritize framework implementation based on their regulatory environment and AI deployment patterns.

High-impact controls drive maximum coverage

Certain AEGIS controls demonstrate exceptional regulatory reach, appearing across multiple frameworks with high frequency. These represent the load-bearing elements of AI governance programs:

The most frequently cited requirements include ISO 8.1 (operational planning and control) with 29 references, NIST MEA2.4 (monitor production systems) and NIST MAN2.4 (deactivate AI systems) with seven references each, and OWASP LLM08 (vector and embedding weaknesses) with six references. EU AI Act Articles 13, 16-18, and 25 each receive four citations.

These high-frequency controls form the foundation of trustworthy AI systems. Control GRC-01 (AI Governance and Oversight Function) maps to 33 regulatory items, making it the single most comprehensive control. Controls GRC-08, DATA-01, DEV-01, and GRC-02 each map to over 20 items, covering governance structures, data integrity, development practices, and oversight mechanisms.

Strategic implementation roadmap

Rather than attempting to satisfy all frameworks simultaneously, AEGIS provides a sequenced approach to AI governance implementation. Security leaders should consider this three-tier strategy:

Establish universal foundations with NIST and ISO
These frameworks provide complete AEGIS coverage and represent the most widely accepted AI governance standards globally. NIST’s risk management approach offers practical guidance for identifying and mitigating AI risks, while ISO/IEC 42001 provides operational structure for managing AI systems within organizational contexts. Starting here ensures broad regulatory alignment with minimal framework complexity.

Deepen compliance through EU and OWASP integration
The EU AI Act adds legal enforceability and specific transparency requirements, particularly crucial for organizations operating in European markets or deploying high-risk AI applications. OWASP’s LLM Top 10 addresses technical vulnerabilities specific to generative AI systems, covering risks like prompt injection attacks and model abuse that broader frameworks don’t fully address. These additions provide regulatory depth and technical specificity.

Prioritize high-density controls for maximum impact
Controls mapping to 20 or more regulatory references offer the greatest compliance leverage. GRC-01 through GRC-08, along with DATA-01 and DEV-01, provide broad regulatory coverage with concentrated effort. These controls address governance structures, data management, development practices, and oversight mechanisms—the core elements of trustworthy AI systems.

Practical implications for AI governance

The AEGIS framework addresses a fundamental challenge in enterprise AI adoption: how to build trustworthy AI systems while navigating an increasingly complex regulatory landscape. By providing a unified control structure that maps to multiple frameworks, AEGIS reduces the compliance burden while improving governance outcomes.

For organizations deploying AI agents—autonomous systems that can take actions on behalf of users—this unified approach becomes particularly valuable. AI agents represent a new frontier in enterprise AI, requiring governance frameworks that address both traditional AI risks and the unique challenges of autonomous decision-making.

The framework’s emphasis on high-density controls also provides practical guidance for resource-constrained security teams. Rather than attempting comprehensive implementation across all frameworks simultaneously, organizations can focus on controls that provide maximum regulatory coverage, then layer in additional requirements based on their specific operational context and regulatory environment.

Security professionals implementing AEGIS should expect this framework to evolve as AI governance standards mature and new regulatory requirements emerge. However, the foundation it provides—grounded in universally accepted standards like NIST and ISO—offers a stable platform for building comprehensive AI governance programs that can adapt to future regulatory developments.