Framework Mapping

Microsoft ACS OWASP Agentic Top 10 NIST AI RMF EU AI Act Deloitte Trustworthy AI EY Responsible AI

Microsoft Agent Control Specification

Open-source agent control spec / governance toolkit

Microsoft's open-source spec defines deterministic allow/deny policy for AI agents at five lifecycle checkpoints, with mandatory logging, human-approval hooks, and regulatory mapping. Prove7 enforces at the same points natively.

Agent Control Specification	Prove7 control
Deterministic allow / deny	Default-deny policy engine; every action is permitted only by an explicit, evaluated rule
Checkpoint 1 — Input	Agent-entry gate: identity + intent + RBAC verified before anything runs
Checkpoint 2 — LLM / model	Inference gate: prompt/data-protection guards and policy on the model call
Checkpoint 3 — State	State/memory governance with provenance and change auditing
Checkpoint 4 — Tool execution	Connector/tool-out gate: per-tool entitlements, default-deny on unapproved tools
Checkpoint 5 — Output	Output gate: policy + classification checks before results leave the boundary
Human approval for designated actions	Approval gates with assignee, deadline, and escalation
Mandatory decision logging	Hash-chained, tamper-evident audit of every decision
Signed plugin / marketplace supply chain	Signed skills & plugins with registration + trust-tiered promotion gating
Regulatory mapping (EU AI Act, HIPAA, SOC 2)	One control plane that maps the same evidence across frameworks

OWASP Top 10 for Agentic Applications

OWASP GenAI Security Project · Dec 2025

The agent threat model the industry is standardizing on. Prove7's Seven Gates and Governed Execution Layer are built to close each risk.

OWASP agentic risk	Prove7 mitigation
1. Agent Goal Hijack	Intent attribution binds each agent to an approved purpose; drift from intent is detected and gated
2. Identity & Privilege Abuse	Cryptographic agent identity + RBAC scoped per org/tenant/instance; least-privilege enforced
3. Unexpected Code / Tool Execution	Default-deny tool-out gate; only registered, entitled connectors run
4. Insecure Inter-Agent Communication	Federated identity + governed, tenant-scoped agent-to-agent calls
5. Human–Agent Trust Exploitation	Human-in-the-loop approval gates; live trust surfaced; no silent escalation
6. Tool Misuse & Exploitation	Per-tool entitlements, rate limits, and enforcement at the connector boundary
7. Agentic Supply-Chain Vulnerabilities	Signed skills/plugins, registration + promotion gates, provenance
8. Memory & Context Poisoning	Input data-protection guards; provenance on context; audited state changes
9. Cascading Failures	Kill switch + circuit-breaking; trust-weighted enforcement halts propagation
10. Rogue Agents	Continuous trust scoring + revoke/kill switch; default-deny for unregistered or regressed agents

NIST AI Risk Management Framework

AI RMF 1.0 (AI 100-1) + Generative AI Profile

The U.S. voluntary standard, organized around four functions. Prove7 runs all four as a continuous runtime loop rather than a periodic review.

NIST AI RMF	Prove7 control
Govern — policies & processes	Policy-as-code with governed promotion (Build → Decide → Act)
Govern — accountability & roles	RBAC (platform/super/org admins), ownership, segregation of duties, kill switch
Govern — third-party & supply chain	Signed skills/plugins, registration & trust-gated promotion
Map — context & system boundaries	Discovery of agents, workflows, and tools across runtimes and clouds
Map — intended use & identity	Cryptographic identity + intent attribution to approved templates
Measure — assess & evaluate risk	Agent Trust Score™, eval suites, conformance baselining
Measure — track over time	Continuous drift detection and live trust telemetry
Manage — prioritize & treat risk	Runtime enforcement: allow, throttle, gate, or kill
Manage — monitor & respond	Tamper-evident audit, remediation, and phase regression
Generative AI Profile risk categories	Mapped to enforced, live runtime controls

EU AI Act

Regulation (EU) 2024/1689 — high-risk requirements

For higher-risk AI, the Act sets requirements across the lifecycle. Prove7 enforces the controls and emits the records those obligations call for. (References are illustrative; confirm scope with counsel.)

EU AI Act obligation	Prove7 control
Risk-management system (Art. 9)	Continuous, per-agent runtime risk management via trust scoring + enforcement
Record-keeping / logging (Art. 12)	Tamper-evident, hash-chained audit of every decision and gate
Transparency & information (Art. 13)	End-to-end provenance and decision lineage
Human oversight (Art. 14)	Approval gates and kill switch enforced before/over agent action
Accuracy, robustness & cybersecurity (Art. 15)	Conformance baselining, OWASP agentic coverage, default-deny gates
Deployer monitoring / post-market	Continuous trust scoring + live drift detection in production
GPAI transparency & documentation	Agent/model documentation captured and attested in the trust record

Deloitte Trustworthy AI™

Six-dimension framework

Deloitte's framework defines six dimensions of trust applied across the AI lifecycle. Prove7 turns each into a measured, enforced control.

Deloitte Trustworthy AI	Prove7 control
Governance across the lifecycle	Runtime governance engine spanning Build → Decide → Act → Audit
Continuous monitoring	Continuous enforcement — gates and trust-derived actions, not just alerts
Fair & impartial	Output policy checks and bias/quality monitoring
Robust & reliable	Eval gates and conformance baselining before promotion
Transparent & explainable	Five-layer decision lineage for every action
Safe & secure	Default-deny gates, OWASP coverage, kill switch
Respectful of privacy	Data classification and data-protection guards at the boundary
Responsible & accountable	Identity, RBAC, ownership, and an immutable audit trail
Governance policies	Agent-executable policies — written once, enforced on every action
Compliance reporting	Real-time compliance decisions at runtime, not retrospective reports
Human review	Autonomous policy mediation, with human approval gates where required
Evidence collection	Cryptographic provenance + attestations on every decision

EY Responsible AI

Responsible AI principles · monitoring · EY.ai Confidence Index

EY pairs nine Responsible AI principles with continuous monitoring and a lifecycle Confidence Index. Prove7 makes monitoring enforceable.

EY Responsible AI	Prove7 control
Responsible AI	Continuous Agent Trust measurement — trust quantified per agent, per call
AI lifecycle governance	7-Proves runtime governance across Build → Decide → Act → Audit
Continuous monitoring	Continuous policy enforcement — gates, not just alerts
Risk assessment	Live Trust computation driving automated decisions
AI inventory	Agent identity & provenance — cryptographic, discovered, and tracked
Governance framework	Trust execution fabric in the path of every action
Compliance reporting	Real-time, inline compliance decisions — not retrospective reports
Human oversight / HITL	Autonomous policy mediation, with human approval gates where required
Audit evidence	Cryptographic attestations & provenance on every decision
AI governance	Agentic system governance — purpose-built for autonomous agents

Attribution. NIST AI Risk Management Framework (AI RMF 1.0) and the Generative AI Profile are publications of the U.S. National Institute of Standards and Technology. The EU AI Act (Regulation (EU) 2024/1689) is legislation of the European Union. Trustworthy AI™ is a framework of Deloitte. Responsible AI and the EY.ai Confidence Index are frameworks of Ernst & Young (EY). The Agent Control Specification and Agent Governance Toolkit are open-source projects of Microsoft. The OWASP Top 10 for Agentic Applications is a project of the OWASP GenAI Security Project. All names, marks, and frameworks are the property of their respective owners. The mappings on this page indicate framework alignment and interoperability only — they do not imply review, certification, sponsorship, or endorsement of Prove7 by any of these organizations, and are not legal or compliance advice.