AI agent governance use cases.
Solve the real enterprise problems created by AI agents.
AgentGuardian helps organizations manage agent sprawl, test agent behavior, enforce runtime controls, and produce evidence for AI governance.
AI agents create new operating risk.
AI agents can access tools, retrieve data, use memory, execute workflows, interact with other agents, and act across business systems. That creates new questions for enterprise teams.
Deep-dive verticals.
Each vertical maps the regulated agent surface, the dominant probe families, and the evidence-pack format that auditors actually accept.
Govern agentic AI under EU AI Act, SR 11-7, NYDFS Part 500.
KYC-AML triage agents, agentic credit scoring, bank-to-bank A2A flows. Regulator-ready evidence packs mapped to OWASP ASI 2026.
- EU AI Act Art. 26
- Fed SR 11-7
- NYDFS Part 500
- MAS FEAT
Red-team clinical decision-support agents without touching PHI.
EHR copilots, memory-poisoning probes, PHI exfiltration via prompt injection, and multimodal claims-photo attacks. HIPAA-aware evidence.
- HIPAA Security Rule
- FDA AI/ML SaMD
- OWASP ASI06
- MITRE ATLAS
Red-team claims and underwriting agents.
End-to-end agentic claims processing, parametric underwriting, life and health risk-rating. Multimodal probes, goal-drift probes, NAIC-ready evidence.
- NAIC AI Model Bulletin
- OWASP ASI10
- AIVSS
- Multimodal
Secure multi-tenant agentic copilots.
Salesforce Agentforce, Microsoft Copilot Studio, ServiceNow Now Assist. MCP rug-pull, A2A replay, and indirect prompt injection probes for SaaS vendors.
- MCP
- A2A v0.3
- OWASP ASI01
- ASI07
Agent security for OT-adjacent IT.
Operations assistants and tier-1 SOC agents against MCP rug-pulls, rogue plugins, and multi-turn social-engineering. NIS2-ready evidence pack signed offline.
- NIS2
- IEC 62443
- OWASP ASI04
- MITRE ATLAS
Find and reduce AI agent attack surface.
Security teams need visibility into the real risks created by autonomous and tool-using AI systems.
- —Discover sanctioned and shadow agents
- —Identify risky tools and permissions
- —Test prompt injection and jailbreak exposure
- —Test unsafe tool calls
- —Assess RAG and memory poisoning risks
- —Detect sensitive data leakage paths
- —Prioritize findings with AIVSS scoring
- —Integrate results into security workflows
Security teams gain a practical way to identify, test, and reduce AI agent risk before it becomes production exposure.
Explore Agent Security AssessmentTurn AI governance into evidence.
Risk and compliance teams need more than AI policy documents. They need proof that AI agents are tested, controlled, monitored, and reviewed.
- —Map agents to governance requirements
- —Classify risk using evidence-backed assessments
- —Track risk posture over time
- —Manage policy exceptions
- —Document remediation status
- —Produce evidence packs for review
- —Support AI governance committees
- —Prepare for audit and regulatory conversations
Risk teams gain a repeatable evidence process for AI agent governance.
Explore Compliance EvidenceHelp developers build agents safely.
AI platform teams need to enable innovation without allowing unmanaged agents to spread across the organization.
- —Provide open-source red teaming for developers
- —Standardize pre-production testing
- —Create CI/CD checks for agent risk
- —Define enterprise policy for agent behavior
- —Manage agent onboarding into production
- —Monitor deployed agents
- —Reduce friction between builders and governance teams
Platform teams can support agent adoption while keeping production environments governed.
Explore EnterpriseFind the AI agents already running in your enterprise.
AI agents can appear across business units, SaaS tools, cloud environments, automation scripts, copilots, internal applications, and developer experiments.
- —Identify known and unknown agents
- —Classify agents by business function
- —Map owners and deployment context
- —Identify unmanaged agents
- —Review tool and data access
- —Establish a governance baseline
- —Prioritize high-risk agents for review
Organizations gain visibility into agent sprawl before it becomes unmanaged enterprise risk.
Book an Agent Sprawl AssessmentTest agents before production.
Before agents move into production, teams need to know whether they can be manipulated into unsafe behavior.
- —Direct prompt injection
- —Indirect prompt injection
- —Unsafe tool calls
- —Excessive permissions
- —Tool argument manipulation
- —RAG poisoning
- —Memory poisoning
- —Sensitive data leakage
- —Agent-to-agent compromise
- —Cascading failures · unsafe autonomy
Teams receive evidence-backed findings, AIVSS scores, remediation guidance, and clear production-readiness signals.
Book a Security AssessmentControl what agents can do in production.
Testing alone is not enough when agents can call tools, access data, and trigger workflows.
- —Which tools agents can use
- —Which actions require approval
- —Which data access paths are allowed
- —Which model calls are permitted
- —Which agent-to-agent interactions are trusted
- —Which exceptions need review
- —Which actions must be logged
Organizations can reduce unsafe agent actions while still allowing teams to deploy useful agentic workflows.
Explore Runtime PolicyProduce evidence for audit and governance review.
Organizations need a defensible way to show how AI agents are tested, governed, monitored, and controlled.
- —Agent inventory records
- —Assessment scope
- —Attack transcripts
- —AIVSS scores
- —Findings by severity
- —Runtime policy decisions
- —Remediation status
- —Approval records
- —Governance mapping
- —Verification records
Audit, risk, and governance teams receive reviewable evidence instead of screenshots, spreadsheets, and manual summaries.
Request Sample Evidence PackMove from agent experimentation to
governed adoption.
See how AgentGuardian supports security, risk, compliance, and AI platform teams.