Python: feat: Add AgentAuditHandler for EU AI Act compliance

[kuriangeorgebinu]

Addresses issue #14034
Initial implementation of AgentAuditHandler middleware for recording and tracking AI agent actions for compliance purposes. Features:

• Audit trail logging for all agent actions
• EU AI Act compliance support (Articles 12, 73, 9, 11)
• Integration framework for AgentAudit AI service
• Compliance report generation capability
• Immutable audit record structure This is a foundational implementation open for community feedback on:
• Design approach for the audit handler interface
• Integration points with agent execution pipeline
• Compliance requirements mapping to implementation
• Blockchain audit trail integration strategy Co-authored-by: GitHub Copilot

Motivation and Context

Description

Contribution Checklist

• The code builds clean without any errors or warnings
• The PR follows the SK Contribution Guidelines and the pre-submission formatting script raises no violations
• All unit tests pass, and I have added new tests where possible
• I didn't break anyone 😄

[Copilot]

Pull request overview

Note

Copilot was unable to run its full agentic suite in this review.

Introduces a new AgentAuditHandler scaffold under semantic_kernel.agents.audit intended to provide an EU AI Act compliance audit trail for agent actions. The core logging and reporting logic is not yet implemented (TODOs).

Changes:

• Adds a new audit subpackage under semantic_kernel.agents.
• Introduces AgentAuditHandler class with initialize, log_agent_action, generate_compliance_report, and close methods (mostly stubs).
• Exports AgentAuditHandler from the package's __init__.py.

Reviewed changes

Copilot reviewed 2 out of 2 changed files in this pull request and generated 6 comments.

File	Description
python/semantic_kernel/agents/audit/init.py	Creates the new audit package and exposes AgentAuditHandler.
python/semantic_kernel/agents/audit/agent_audit_handler.py	Adds the new handler class with placeholder implementations.

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[github-actions]

Automated Code Review

Reviewers: 4 | Confidence: 91%

✓ Correctness

The existing unresolved review comments comprehensively cover the significant correctness issues in this PR (timestamp not defaulted despite docstring claim, initialize() not validating connectivity, log_agent_action being a misleading no-op, sensitive data logging, Optional style inconsistency, and missing tests). Beyond those already-flaged issues, I found no additional correctness bugs. The code is internally consistent for a stub implementation, but the already-identified issues remain valid blockers for a compliance-oriented component.

✓ Security Reliability

The primary new security concern (beyond the already-flagged issues) is that the api_key is stored as a plain string attribute, contradicting the established codebase pattern of using Pydantic's SecretStr. This means the credential can be exposed via object repr, dict access, serialization, or any inadvertent logging of the handler instance — particularly problematic for a module whose purpose is security compliance.

✓ Test Coverage

This PR adds a new AgentAuditHandler class with zero accompanying unit tests. The repository has a well-established pattern of unit tests under python/tests/unit/agents/ for every agent module (e.g., test_agent.py, test_bedrock_agent.py, test_azure_ai_agent.py). The new audit module ships without any test coverage, meaning all code paths (initialize success/failure, log_agent_action when disabled/uninitialized/valid, generate_compliance_report when disabled/uninitialized/valid, and close) are untested. This is a blocking gap for a compliance-oriented component where correctness is critical.

✗ Design Approach

The main design issue is that this PR introduces a standalone audit helper, but it does not integrate with the agent runtime’s existing interception pipeline, so it cannot deliver the stated goal of auditing all agent actions on the normal execution path. I did not find other design findings that met the repo’s evidence bar without duplicating existing review comments.

Flagged Issues

• The new audit handler is not integrated with the runtime-wide InterventionHandler hook that already intercepts every send/publish/response in InProcessRuntime. The repo's established pattern (python/semantic_kernel/agents/runtime/core/intervention.py:29-39) is explicitly designed for components that "modify, log or drop messages." Because AgentAuditHandler is a standalone class, normal agent traffic will never reach it automatically, and the stated goal of auditing all agent actions is not achieved by the current design.

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Automated review by kuriangeorgebinu's agents

[github-actions]

Security: api_key is stored as a plain str attribute, which means it can leak via repr(), __dict__, serialization, or accidental logging of this object. The rest of the SK Python codebase (e.g., openai_assistant_agent.py) uses Pydantic's SecretStr to wrap API keys. For a compliance-oriented module this is especially important—store as SecretStr and call .get_secret_value() only when transmitting to the service.

Suggested change

	self.enabled = enabled
	self.api_key: SecretStr | None = SecretStr(api_key) if api_key else None

[github-actions]

The service_url is logged at INFO level. If a user supplies a URL with embedded credentials or tokens as query parameters (e.g., https://service.example.com?token=secret), those will be written to application logs. Consider logging only the scheme+host or omitting the URL entirely.

Suggested change

	self._validated = True
	logger.info("AgentAuditHandler initialized successfully")