AI Agent Runtime Security

Runtime security for AI agents

Discover shadow agents, bind them to identity and policy, enforce risky actions outside the agent, and produce audit-ready evidence for every decision.

Vigilis Security Dashboard

Live

Monitoring active

Shadow AI Reduction

unmanaged agents found

Policy Coverage

158/158

governed identities

Approval Governance

pending approvals

Audit Readiness

ledger events with chain evidence

Runtime Enforcement

4/4

planes active

AgentIdentityPolicy StatusLast ActionDecision

finance-analyst-01svc-finance@corpGovernedAPI Call to Stripe

Allowed

code-assistant-03dev-team@corpGovernedFile Write to /data

Held

shadow-agent-47UnknownUnmanagedExternal Webhook

Blocked

support-bot-02support@corpGovernedDB Query

Allowed

AI agents now act with real authority

They use tools, credentials, APIs, cloud roles, Kubernetes workloads, files, network destinations, and provider actions. Prompt guardrails are not enough.

Credentials & APIs

Agents access tools, APIs, cloud roles, and provider actions with enterprise credentials.

Bypassed Guardrails

Compromised prompts, tools, or agent logic can bypass app-layer controls.

Shadow Agents

Unapproved agents create unmanaged infrastructure risk across the enterprise.

Evidence Required

Regulated organizations need proof that controls operated, not just policy documents.

Prompt guardrails are not enough

Application-layer guardrails depend on the agent behaving correctly. Vigilis enforces policy outside the agent.

Application Guardrails

Depend on the agent behaving correctly
Can be bypassed by prompt injection, compromised tools, or agent logic
Have limited visibility into runtime behavior
Are hard to prove to auditors

Vigilis Runtime Security

Enforces outside the agent
Observes tools, files, processes, network, cloud, SaaS, and identity-provider activity
Holds, denies, allows, or contains risky actions
Produces ledger-backed evidence

What Vigilis Proves

Six questions every regulated enterprise must answer about their AI agents.

Which agents exist

Discover all AI agents including shadow and unapproved deployments.

Who owns them

Track ownership, environment, and organizational accountability.

What identity or credential they use

Map agents to enterprise identity, shared-service-account risk, and runtime trust posture.

What policy governs them

Associate each agent with a policy bundle and enforcement configuration.

What runtime actions were allowed, held, or blocked

Track every decision: allow, hold for approval, deny, or contain.

What evidence proves the control operated

Generate ledger-backed evidence packs for audit and incident review.

Make every agent governable

Comprehensive visibility and control across the entire AI agent lifecycle.

Shadow Agent Discovery

Find registered and shadow AI agents across enterprise environments automatically.

Agent Identity Governance

Track owner, environment, mapped enterprise identity, shared-service-account risk, policy bundle, and runtime trust posture.

Runtime Policy Enforcement

Allow, deny, require approval, or contain risky actions in real time outside the agent.

Human Approval Workflow

Route high-risk actions to human reviewers before execution with full context.

Runtime Containment

Isolate compromised or misbehaving agents without disrupting other workloads.

Audit-Ready Evidence

Generate evidence packs for governance, incident review, and regulatory audits.

Multi-plane runtime enforcement

Enforcement outside the agent across provider, Kubernetes, endpoint, and kernel planes.

AI Agent

Provider Guard

Kubernetes Guard

Endpoint Guard

Kernel Enforcer

Evidence Ledger

Provider Guard

Controls cloud, SaaS, and identity-provider actions such as secrets, roles, tokens, and privileged changes.

Kubernetes Guard

Admission control, workload-scoped rollout, and sidecar egress mediation.

Endpoint Guard

Caller-shim and signed offline policy cache for host-level runtime control.

Kernel Enforcer

BPF/LSM enforcement path for Linux file and process controls.

Evidence Ledger

Tamper-evident chain of decisions, approvals, and enforcement outcomes.

From risky action to auditable decision

Every agent action flows through a governed decision pipeline.

Observe

Evaluate

Decide

Approve / Deny

Record Evidence

Example: External Data Transmission

An agent attempts to send data to an external webhook. Vigilis identifies the action, evaluates policy and risk, requires human approval, records the decision, and preserves the evidence trail.

Built for regulated AI adoption

Accelerate enterprise AI adoption without relying only on prompt guardrails.

Reduce shadow AI risk

Prevent over-authorized agents from misusing tools and credentials

Prove runtime controls are operating

Support audit, compliance, and incident review

Accelerate enterprise AI adoption without relying only on prompt guardrails

Designed for regulated enterprises

Meet regulatory expectations with evidence-backed AI governance.

EU AI Act

High-risk AI system governance and documentation

DPDP

Data protection and privacy compliance

RBI Guidelines

Financial sector AI governance requirements

NYDFS Part 500

Cybersecurity and model risk governance

Where Vigilis is different

Comprehensive runtime security that existing solutions cannot provide.

Capability	Prompt Guardrails	API Gateways	Cloud Security	Vigilis
Runtime agent discovery
Agent identity governance
Provider action control
Kubernetes & endpoint enforcement
Kernel-level Linux enforcement
Human approval workflow
Ledger-backed evidence packs
No agent code changes

Make AI agents governable before they scale

Vigilis helps enterprises move from AI experimentation to controlled, auditable agent operations.