Government and public sector organizations adopting AI agents must meet stringent security and governance requirements: NIST AI Risk Management Framework alignment, FedRAMP-compatible architecture, data sovereignty guarantees, and transparent audit trails. SafeClaw by Authensor provides deny-by-default action gating that runs entirely locally, produces hash-chained audit logs, and has zero external dependencies — meeting the core requirements for government AI agent governance. Install with npx @authensor/safeclaw.

Government AI Agent Requirements

Public sector AI agent deployment differs fundamentally from private sector adoption:

• NIST AI RMF compliance — the AI Risk Management Framework requires organizations to GOVERN, MAP, MEASURE, and MANAGE AI risks. Agent action gating addresses the GOVERN and MANAGE functions.
• Data sovereignty — government data must remain on controlled infrastructure. No data can flow to third-party services without authorization.
• Authority to Operate (ATO) — AI agent tooling must pass security review. Zero dependencies and open source licensing simplify this process.
• Transparency and explainability — every agent decision must be traceable. Policy-as-code provides deterministic, auditable decision logic.
• Supply chain risk management — Executive Order 14028 requires software supply chain security. Zero-dependency tools minimize supply chain attack surface.

Government-Grade SafeClaw Policy

# safeclaw.yaml — government / public sector policy
version: 1
default: deny

rules:
  # Controlled source code access
  - action: file_read
    path: "src/**"
    decision: allow
    reason: "Application source code"

- action: file_write
    path: "src/**"
    decision: prompt
    reason: "All code changes require human approval"

# Classified and sensitive data protection
  - action: file_read
    path: "data/classified/**"
    decision: deny
    reason: "Classified data: no agent access"

- action: file_read
    path: "data/cui/**"
    decision: deny
    reason: "Controlled Unclassified Information blocked"

- action: file_read
    path: "data/pii/**"
    decision: deny
    reason: "PII access blocked"

- action: file_read
    path: "*/.env"
    decision: deny
    reason: "Environment secrets blocked"

- action: file_read
    path: "*/credential*"
    decision: deny
    reason: "Credential files blocked"

- action: file_read
    path: "*/secret*"
    decision: deny
    reason: "Secret files blocked"

# Complete network lockdown
  - action: network_request
    destination: "*"
    decision: deny
    reason: "No outbound network access"

# Shell restrictions
  - action: shell_execute
    command: "npm test"
    decision: allow
    reason: "Tests are safe"

- action: shell_execute
    command: "sudo *"
    decision: deny
    reason: "No privilege escalation"

- action: shell_execute
    command: "curl *"
    decision: deny
    reason: "No outbound data transfer"

- action: shell_execute
    command: "wget *"
    decision: deny
    reason: "No outbound data transfer"

- action: shell_execute
    command: "ssh *"
    decision: deny
    reason: "No SSH connections"

- action: shell_execute
    command: "scp *"
    decision: deny
    reason: "No file transfers"

- action: shell_execute
    command: "rm -rf *"
    decision: deny
    reason: "No recursive deletion"

- action: shell_execute
    command: "chmod *"
    decision: deny
    reason: "No permission changes"

This policy implements the most restrictive posture: agents can read source code and run tests, but every other action is either denied or requires explicit human approval.

NIST AI RMF Mapping

SafeClaw's capabilities map to specific NIST AI RMF functions:

| NIST AI RMF Function | SafeClaw Implementation |
|----------------------|------------------------|
| GOVERN 1.1 — Legal and regulatory requirements | Policy-as-code enforcing data access boundaries |
| GOVERN 1.5 — Risk management process | Deny-by-default with first-match-wins evaluation |
| MAP 3.5 — Likelihood of known risks | Simulation mode to assess agent behavior before deployment |
| MEASURE 2.6 — Measurement of AI risks | Audit trail quantifying denied vs. allowed actions |
| MANAGE 2.2 — Risk response mechanisms | Real-time action gating with deny/prompt/allow decisions |
| MANAGE 4.1 — Monitoring post-deployment | Hash-chained audit logs for continuous monitoring |

Data Sovereignty and Local Execution

SafeClaw runs entirely on local infrastructure. The policy engine evaluates actions locally. The audit trail is stored locally. No telemetry, no cloud API calls, no external dependencies. This architecture is compatible with air-gapped environments, SCIF requirements, and data sovereignty mandates.

For government procurement, SafeClaw's MIT license eliminates vendor lock-in and licensing complexity. The open source codebase allows government security teams to conduct full source code review. The 446-test suite provides assurance of policy engine correctness. SafeClaw works with both Claude and OpenAI agents, supporting multi-vendor AI strategies common in government.

Related pages:

• NIST AI RMF Gating Compliance
• Zero Trust Agent Architecture
• Data Residency and Local Execution
• Vendor Risk and Zero Dependency
• Hash-Chained Audit Logs