Test every AI agent before it reaches production.
ZeroTrusted.ai validates what an agent can see, what it can call, what it can change, and whether it can be manipulated into leaving its mission scope—across customer-hosted and cloud AI environments.
Current platform coverage
Numbers customers can verify
The catalog separates registered capabilities from executable handlers. Readiness is checked against the customer profile, connector credentials, runtime image, authorization boundary, and target—not inferred from a static inventory.
registered agents
Governed identities with model, permission, tool, and version metadata.
tool definitions
55 catalog categories spanning SOAR, GRC, AppSec, cloud, AI security, and intelligence.
wired handlers
Executable tool paths verified per tenant, connector, authorization scope, and runtime.
packaged frameworks
Pre-built mappings for federal, defense, healthcare, financial, energy, and AI governance requirements.
AI attack readiness
The attacker is autonomous. Your validation must be too.
Traditional testing checks an application. Agentic testing checks the entire operating envelope: model behavior, memory, tools, permissions, data boundaries, human approvals, and the actions an agent can coordinate across your stack.
Agent and model preflight
Verify the selected model, version, provider, tools, connectors, credentials, boundaries, and fallback plans before a mission starts.
Adversarial agent testing
Probe prompt injection, jailbreaks, data exfiltration, excessive agency, unsafe tool arguments, and agent-to-agent manipulation.
Shadow and embedded AI discovery
Find unmanaged browser, API, Kubernetes, RAG, endpoint, and embedded-model usage and connect each system to an owner and policy.
Mission trace and Agent-BOM
Record the model, agent version, permissions, tools, inputs, outputs, approvals, errors, and evidence needed to reproduce a decision.
Remediation and retest
Create a bounded remediation plan, route high-impact changes for approval, then rerun the failed tests before certification.
Audit-ready evidence
Export signed, hashed evidence packages mapped to NIST AI RMF, NIST SP 800-53, ISO 42001, CMMC, FedRAMP, and sector requirements.
Recent platform developments
Built for the next wave of autonomous attacks
Recent releases focus on proving that the right model, tool, connector, evidence path, and authorization are available before an agent acts.
Agent fleet certification
One-button preflight, full verification, actionable readiness remediation, and reconciliation of registered, orphaned, and quarantined agents.
Real execution evidence
SAST/DAST, cloud evidence, connector live tests, and asynchronous scanner jobs now preserve tool receipts and honest partial-failure states.
Autonomous attack-chain defense
Guarded orchestration correlates identity, endpoint, network, cloud, vulnerability, and threat signals before approval-gated containment.
Model integrity and governance
Hidden-weight scanning, live provider model discovery, Agent-BOM snapshots, mission traces, token telemetry, and policy-aware model assignment.
Multi-cloud and sector GRC
Cloud evidence crosswalks connect AWS, Azure, GCP, Kubernetes, and sector control requirements to reports, remediation, and retesting.
Connector reliability
OAuth client-credential flows, ServiceNow/Tenable/Nessus/CrowdStrike paths, threat-intelligence provisioning, and diagnostic error causes reduce silent failures.
Regulatory and technology coverage
Map evidence to the requirements your customers already use
Packaged mappings cover FedRAMP High and Moderate, NIST SP 800-53 Rev. 5, NIST RMF and SP 800-37, CMMC 2.0, FISMA, DISA STIG, CIS Benchmarks, SOC 2, ISO 27001/27002, ISO 42001, NIST AI RMF, PCI DSS, HIPAA, NERC CIP, GDPR, CCPA, GLBA, BSA/AML, FFIEC, EU AI Act, APPI/METI, and Brazil LGPD. Evidence workflows also support OSCAL, SCAP, XCCDF/OVAL, STIX/TAXII, MITRE ATT&CK/ATLAS, OWASP, and cloud-native Kubernetes controls.
AI Firewall KPIs
Policy decisions, block/allow rates, redaction coverage, prompt-injection and jailbreak detections, tool-call violations, latency, and model/provider attribution.
AI Healthcheck KPIs
Drift, bias, factuality, regression, robustness, data-integrity, vulnerability, configuration drift, and evidence freshness over time.
AI SOAR and governance KPIs
Agent readiness, tool reliability, MTTD/MTTR, findings by tenant and control, remediation aging, retest status, token cost, and audit evidence completeness.
One governed lifecycle
Discover → Test → Certify → Retest
Every result is tied to a customer profile, agent version, model assignment, tool permission, operator, and evidence package.
Discover
Inventory agents, models, tools, endpoints, embedded AI, and data flows.
Preflight
Check access, connectors, model availability, guardrails, and mission scope.
Test
Run deterministic, adversarial, and degraded-condition test packs.
Remediate
Assign issues to owners and require approval for privileged changes.
Certify
Issue an A/B/C readiness result with expiry and evidence.
Retest
Continuously validate drift, new tools, new models, and policy changes.
Govern every model, agent, and tool call
Connect agentic testing to the controls customers already use for security and compliance.
Agent-BOM and provenance
Signed snapshots capture agent identity, model/provider, version, tools, connectors, permissions, and deployment location.
Human approval where it matters
High-impact containment, credential, network, and production changes stay in an explicit approval queue.
Evidence that survives review
SHA-256 evidence chains and exportable traces support authorizing officials, auditors, and customer security teams.
Ready to certify your AI agents?
Start with one mission, one model, or one embedded AI workload. Expand to continuous testing across your enterprise and MSSP customer profiles.
Schedule a readiness reviewSee Shadow & Embedded AI security →