The Platform

AI Runtime Control
with Evidence Built In.

Pre-ingestion trust control for AI agents and enterprise AI workflows. CyberArmor connects URL trust gating, detection, policy, routing, identity, enforcement, response, and evidence into a single runtime control system — with proof at every decision point.

Request a Demo URL Trust Gate — 15-min PoC

Platform Architecture — AI Security Runtime

Identify

Gate

Inspect

Decide

Control

Respond

Prove

External URLs & ContentUsersApplicationsAI AgentsAPIsProvidersModelsDataEvidence

Coverage depends on deployment pattern; the architecture is designed to connect the layers as control points mature.

Seven Runtime Capabilities.
One Integrated Control Loop.

Each capability can support a focused pilot, but the real value appears when the pieces work together: pre-ingestion trust gating, identity, detection, policy, routing, response, and evidence in one explainable flow.

URL & Context Trust Gate

A pre-ingestion control point that evaluates URLs and external content before a human, browser, endpoint agent, RASP-instrumented app, or AI agent fetches them. Existing URL filters answer 'is this site malicious for a human?'; CyberArmor.AI also answers 'is this content safe for an AI agent to ingest?' A 15-minute local PoC is available.

Key Capabilities

  • Canonicalisation, querystring redaction, and homoglyph / punycode checks before any network call
  • SSRF-guarded safe crawl plus optional Playwright detonation to surface CSS-hidden, off-screen, and Unicode-tag-encoded promptware
  • Detection-service fan-out for phishing, hidden prompt injection, promptware, data-exfil, and IOC scoring with optional Safe Browsing v4, Microsoft SmartScreen, and VirusTotal reputation feeds
  • Policy decisions across allow, warn, redact, sandbox, block, and isolate — with evidence written to audit

AI Asset Discovery & Inventory

You cannot control what you cannot see. CyberArmor.AI uses supported endpoint, browser, API, and integration signals to surface AI tools, model calls, provider connections, and agent activity.

Key Capabilities

  • Discovery signals for shadow AI tools and unauthorized model connections
  • Tenant-scoped inventory views for AI systems, APIs, agents, and workloads
  • Monitoring for new AI activity and deployment drift in supported paths
  • Expansion across SaaS, identity, cloud, and on-prem sources through design-partner work

Policy Enforcement Engine

CyberArmor.AI translates governance requirements into executable policy decisions tied to tenant, actor, workload, model, provider, data, and risk context.

Key Capabilities

  • Tenant-scoped policy rules for AI access, routing, redaction, blocking, and monitoring
  • Context-sensitive evaluation based on risk posture, data sensitivity, provider, and actor context
  • OPA-backed evaluation paths with fallback behavior for pilot validation
  • Artifact references and policy outcomes that can be preserved in evidence records

Runtime Protection

Runtime protection means acting when AI activity happens, not simply reviewing logs later. CyberArmor.AI connects detection and policy to approved enforcement outcomes in supported control points.

Key Capabilities

  • Inspection of AI API calls, model queries, prompt fields, SDK requests, and agent actions where deployed
  • Prompt injection detection, credential leak detection, and sensitive-data inspection
  • Adaptive enforcement: monitor, warn, block, redact in supported paths, route, limit, or redirect
  • Protection patterns for AI chatbots, LLM-powered applications, developer workflows, and autonomous workflows

Identity-Aware Trust Controls

In AI environments, identity is not just about users. CyberArmor.AI models humans, services, workloads, and AI agents so security teams can reason about who or what acted.

Key Capabilities

  • Agent registration, tenant scoping, owner metadata, allowed and denied tools, delegation chains, and revocation paths
  • Service and workload context for AI API access and provider use
  • Cross-domain trust decisions spanning human, non-human, and AI actor types
  • Integration patterns for identity providers and zero trust programs as pilots mature

Evidence & Decision Traceability

CyberArmor.AI is designed to preserve evidence that is attached to controls: actor, request, provider, data classification, policy decision, response action, and downstream trace context.

Key Capabilities

  • Decision-level telemetry for AI actions, model calls, and agent behavior in monitored paths
  • Audit-chain modeling with trace IDs, span IDs, chain hashes, signatures, and previous-event references
  • Incident response acceleration through structured, context-rich evidence
  • Evidence export patterns for SOC, audit, legal, compliance, and executive review

Detection, Enforcement & Response

CyberArmor.AI closes the loop from detection to policy to enforcement to response. When a threat or policy violation is identified, the platform records context and can trigger approved actions.

Key Capabilities

  • Response actions for AI-specific threat scenarios, including block, notify, ticket, webhook, and containment patterns
  • SIEM/SOAR integration workflows moving from pilot into production hardening
  • Structured alert context: policy violated, actor identity, action taken, evidence ID
  • Containment capabilities: redaction-mode response, agent suspension, scope reduction, access revocation, and routing changes
Integrations

Built to Work With Your Existing Stack.

CyberArmor.AI extends your security investment — integrating with the identity, cloud, and security platforms your team already relies on.

Microsoft Entra ID
Okta
AWS
Azure
Google Cloud
Splunk
Palo Alto Cortex
CrowdStrike
ServiceNow
Wiz
+ Many more
Get Started

Ready to Control and Prove AI Activity?
Let's Talk.

See how CyberArmor.AI maps to your AI activity, data leakage risk, agent workflows, provider usage, runtime controls, and evidence needs. The best demos start with the control problem you already have.

No spam. No hard sell. Every request is reviewed personally.