Framework Comparison
ATF is complementary, not competing. It's the implementation specification that other frameworks' threat models and risk assessments lead you to.
“MAESTRO tells you what to worry about. OWASP tells you what can go wrong. NIST tells you the principles. ATF tells you what to build.”
Use threat models and risk assessments to identify gaps. Use ATF to close them.
Jump to Framework
| Framework | Relationship |
|---|---|
| MAESTRO (CSA) | Complementary |
| OWASP Top 10 for Agentic Applications | Complementary |
| NIST 800-207 / AI RMF | Foundational |
| CSA AI Controls Matrix (AICM) | Parent Framework |
| ISO/IEC 42001:2023 | Directly Aligned |
| ISO/IEC 27001:2022 | Foundational |
| AWS Agentic AI Security Scoping Matrix | Directly Aligned |
| OWASP AEGIS (Forrester) | Complementary |
| KPMG TACO | Adjacent |
MAESTRO (CSA)
ComplementaryThreat Modeling for Multi-Agent Systems
MAESTRO tells you what to worry about. ATF tells you what to build. MAESTRO provides a 7-layer threat model for multi-agent systems; ATF provides the governance controls to address those threats.
Mapping to ATF
| MAESTRO | ATF Element |
|---|---|
| Foundation Model | Identity: credential management, model provenance |
| Data Operations | Data Governance: input validation, output filtering |
| Agent Core | Behavior: monitoring, anomaly detection, explainability |
| Agent Ecosystem | Identity: agent-to-agent authentication, trust chains |
| Deployment Infrastructure | Segmentation: network isolation, policy enforcement |
| Orchestration & Interaction | Segmentation + Behavior: workflow controls, coordination limits |
| Evaluation & Observability | All Elements: continuous monitoring, compliance reporting |
When you complete a MAESTRO threat assessment, the natural next question is: 'Now what do we build?' ATF provides that implementation specification. Use MAESTRO to identify risks; use ATF to mitigate them.
OWASP Top 10 for Agentic Applications
ComplementaryRisk Catalog for AI Agent Vulnerabilities
OWASP identifies the top risks; ATF provides the controls to mitigate them. Every OWASP agentic risk maps to one or more ATF elements.
Mapping to ATF
| OWASP Top 10 for Agentic Applications | ATF Element |
|---|---|
| ASI-01: Agent Identity Spoofing | Identity: cryptographic agent credentials, mutual auth |
| ASI-02: Agent Authorization Failures | Identity + Segmentation: RBAC/ABAC, policy-as-code |
| ASI-03: Excessive Agent Autonomy | Behavior + Segmentation: maturity levels, boundaries |
| ASI-04: Improper Output Handling | Data Governance: output validation, toxicity filtering |
| ASI-05: Insecure Agent Memory | Data Governance: data classification, encryption |
| ASI-06: Agent-to-Agent Trust Issues | Identity: trust chains, session verification |
| ASI-07: Insufficient Agent Logging | Behavior: comprehensive structured logging |
| ASI-08: Vulnerable Agent Supply Chain | Identity + Data Governance: provenance, validation |
| ASI-09: Agent Resource Exhaustion | Segmentation: rate limiting, blast radius containment |
| ASI-10: Unreliable Agent Operations | Incident Response: circuit breakers, graceful degradation |
OWASP provides the risk vocabulary; ATF provides the governance response. Together they give security teams both the 'what can go wrong' and the 'how to prevent it.'
NIST 800-207 / AI RMF
FoundationalZero Trust Architecture + AI Risk Management
ATF operationalizes NIST Zero Trust principles for AI agents. NIST 800-207 defines the architecture; ATF applies it to the specific challenges of autonomous, non-deterministic systems.
Mapping to ATF
| NIST 800-207 / AI RMF | ATF Element |
|---|---|
| Never trust, always verify | All Elements: continuous verification at every level |
| Least privilege access | Segmentation: strict allowlists, maturity-based boundaries |
| Assume breach | Incident Response: kill switches, circuit breakers, containment |
| GOVERN function | Operating Model: roles, review cadence, promotion criteria |
| MAP function | Identity + Behavior: agent characterization, behavioral baselines |
| MEASURE function | Behavior: anomaly detection, performance metrics |
| MANAGE function | All Elements: controls, incident response, continuous improvement |
NIST provides the principles; ATF provides the agent-specific implementation. Organizations already implementing Zero Trust will find ATF a natural extension of their existing architecture.
CSA AI Controls Matrix (AICM)
Parent Framework243 AI Security Controls Across 18 Domains
AICM is CSA’s flagship AI security controls framework: 243 control objectives across 18 domains, built on the Cloud Controls Matrix (CCM). ATF operationalizes the agent-specific subset of AICM’s controls, adding the maturity model and progressive autonomy governance that AICM does not have. AICM is the broad AI controls umbrella; ATF is the agent-specific operating model underneath it.
Mapping to ATF
| CSA AI Controls Matrix | ATF Element |
|---|---|
| Identity & Access Management (IAM) | Identity: agent credentials, mutual authentication, non-human identity lifecycle |
| Data Security & Privacy Lifecycle Management (DSP) | Data Governance: input validation, PII/PHI classification, data lineage, output filtering |
| Log and Monitoring (LOG) | Behavior: real-time behavioral monitoring, structured decision logging, anomaly detection |
| Infrastructure & Virtualization Security (IVS) | Segmentation: network isolation, resource boundaries, blast radius containment |
| Security Incident Management (SEF) | Incident Response: kill switches, circuit breakers, containment, recovery playbooks |
| Model Security (MDS) | Identity + Data Governance: model provenance, supply chain verification, integrity validation |
| Application & Interface Security (AIS) | Segmentation + Behavior: API governance, action boundaries, rate limiting |
| Governance, Risk & Compliance (GRC) | Operating Model: governance policies, promotion criteria, review cadence, compliance evidence |
| Supply Chain Management & Transparency (STA) | Identity + Data Governance: agent provenance, tool verification, delegation chain integrity |
| Audit & Assurance (A&A) | Maturity Model: governance sign-off gates, assessment scoring, certification readiness |
| Change Control & Configuration Management (CCC) | Behavior + Operating Model: configuration drift detection, change approval workflows |
| Threat & Vulnerability Management (TVM) | Behavior + Incident Response: threat detection for agent-specific attack vectors |
AICM defines the 243 controls for AI broadly. ATF operationalizes the subset that applies to autonomous agents. The analogy: AICM is to ATF what CCM is to a specific security domain framework. Organizations using AICM for broad AI governance can layer ATF as the agent-specific operating model, getting a maturity progression and scored self-assessment that AICM does not provide. Both are published through CSA, and both are freely available.
ISO/IEC 42001:2023
Directly AlignedAI-Specific Management System Standard
ISO/IEC 42001:2023 is the world’s first AI management system standard, with 39 controls across 10 domains covering AI governance, risk management, system lifecycle, and data management. ATF operationalizes the agent-specific subset of these controls, providing the implementation specification that ISO 42001 governance programs need for autonomous AI systems. Microsoft, AWS, Google Cloud, and Anthropic have all achieved ISO 42001 certification, making this crosswalk increasingly important for enterprise adoption.
Mapping to ATF
| ISO/IEC 42001:2023 | ATF Element |
|---|---|
| A.2 AI Policy | Operating Model: governance policies, promotion criteria, review cadence |
| A.3 Internal Organization | Operating Model: roles, responsibilities, reporting for agent governance |
| A.4 Resources for AI Systems | Identity: agent inventory, credential management, resource documentation |
| A.5 AI System Lifecycle | Maturity Model: Intern → Principal progression, promotion gates, demotion triggers |
| A.6 Data for AI Systems | Data Governance: input validation, data provenance, PII/PHI protection, output filtering |
| A.7 System Information for Interested Parties | Behavior: transparency, decision logging, explainability, audit trails |
| A.8 Use of AI Systems | Segmentation + Behavior: action boundaries, rate limiting, behavioral monitoring |
| A.9 Third-Party and Customer Relationships | Identity + Segmentation: agent-to-agent trust, supply chain verification, delegation chains |
| A.10 Continual Improvement | All Elements: maturity progression, incident-driven demotion, continuous verification |
ISO 42001 provides the management system; ATF provides the agent-specific operating model within it. Organizations pursuing ISO 42001 certification will find that ATF implementation generates much of the evidence and documentation the standard requires for autonomous AI systems.
ISO/IEC 27001:2022
FoundationalInformation Security Management Baseline
ISO/IEC 27001 is the global standard for information security management systems. ATF extends ISO 27001’s security controls to address the unique challenges of autonomous AI agents: non-deterministic behavior, machine-speed decision-making, and dynamic trust relationships. Organizations with existing ISO 27001 certification will find ATF builds naturally on their existing ISMS.
Mapping to ATF
| ISO/IEC 27001:2022 | ATF Element |
|---|---|
| A.5 Organizational Controls (policies, roles) | Operating Model: agent governance policies, ownership, review cadence |
| A.5.15-A.5.18 Access Control | Identity + Segmentation: agent credentials, least privilege, action boundaries |
| A.8.1-A.8.10 Technology Controls (endpoint, logging) | Behavior: continuous monitoring, structured logging, anomaly detection |
| A.8.11-A.8.12 Network Security | Segmentation: network isolation, resource allowlists, blast radius containment |
| A.8.15-A.8.16 Logging and Monitoring | Behavior: real-time behavioral analysis, decision audit trails |
| A.5.24-A.5.28 Incident Management | Incident Response: circuit breakers, kill switches, containment, recovery playbooks |
| A.5.31-A.5.36 Compliance | Maturity Model: governance sign-off gates, compliance evidence generation |
| A.8.25-A.8.31 Secure Development | Data Governance: input validation, output filtering, data lineage |
ISO 27001 provides the information security foundation; ATF extends it for autonomous AI agents. The key addition ATF makes is addressing non-deterministic agent behavior, progressive autonomy, and machine-speed incident response, none of which ISO 27001 was designed to cover.
AWS Agentic AI Security Scoping Matrix
Directly AlignedAgent Scope Classification
ATF maturity levels map 1:1 to AWS scopes. This wasn't accidental. Both frameworks recognize that agent autonomy must be classified and governed progressively.
Mapping to ATF
| AWS Agentic AI Security Scoping Matrix | ATF Element |
|---|---|
| Scope 1: No Agency | Intern: read-only, fully supervised |
| Scope 2: Prescribed Agency | Junior: recommendations, human approval required |
| Scope 3: Supervised Agency | Senior: autonomous within guardrails, post-action notification |
| Scope 4: Full Agency | Principal: self-directed within policy bounds |
AWS provides the scoping model; ATF provides the governance framework to operationalize each scope level with specific controls, promotion criteria, and demotion triggers.
OWASP AEGIS (Forrester)
ComplementaryAgent Governance Assessment
AEGIS focuses on governance assessment and organizational readiness. ATF provides the technical specification that organizations can implement once they understand their governance posture.
Mapping to ATF
| OWASP AEGIS | ATF Element |
|---|---|
| Governance Assessment | ATF Self-Assessment: element-by-element readiness evaluation |
| Organizational Readiness | Operating Model: roles, cadence, documentation |
| Risk Evaluation | Maturity Model: risk profiles per level |
Use AEGIS to assess organizational readiness; use ATF to define what 'ready' looks like technically. The two frameworks address different layers of the same problem.
KPMG TACO
AdjacentTrust in AI Compliance and Oversight
TACO focuses on audit and compliance governance for AI systems broadly. ATF focuses specifically on the operational governance of autonomous agents. Complementary scopes with overlapping concerns.
Mapping to ATF
| KPMG TACO | ATF Element |
|---|---|
| Compliance Oversight | ATF Compliance Mapping: SOC 2, ISO 27001, EU AI Act alignment |
| AI Governance | Operating Model: promotion boards, review cadence |
| Trust Measurement | Maturity Model: trust earned through demonstrated performance |
Organizations implementing TACO for broad AI governance can use ATF as the agent-specific implementation layer, ensuring autonomous systems have the additional controls their autonomy demands.
The Big Picture
Identify Risks
MAESTRO, OWASP, NIST AI RMF
Define Controls
ATF Specification + Maturity Model
Implement
ATF Component Catalog + Patterns