Why a controlled pilot?
Security buyers in regulated industries cannot evaluate AI security tools the same way they evaluate SaaS productivity software. Trust boundaries, data handling, and evidence requirements demand a different model.
Scope is negotiated before deployment
You define which workflows, consumer surfaces, and data flows are in scope. Nothing outside the agreed boundary is inspected or logged.
Evidence-first, not black-box
Every gate decision and runtime enforcement action produces an attributable evidence record. You can review exactly what the system did and why.
Security-led, not sales-led
Pilots are designed with your AppSec or CISO team, not pushed through procurement. We start with the PoC on your hardware before any contract discussion.
Measurable outcome in 30–90 days
A pilot-close readout gives your leadership a measured answer: detection rates, false-positive rates, latency impact, and evidence completeness.
Three programs.
One trust framework.
Each program builds on the one before. Most security-led evaluations start with the URL Trust Gate pilot and expand from there.
Program 01
URL Trust Gate Pilot
Stop hostile web content before it enters AI context.
The problem it solves
Your AI systems fetch, ingest, and act on external URLs. Hidden prompt injection, CSS-concealed instructions, and zero-width-encoded payloads are invisible to existing filters — but read verbatim by LLMs. This pilot deploys the URL Trust Gate in front of one or more AI-connected workflows.
Pilot outcome
A measured, evidence-backed answer to: how much hostile content were your AI systems about to ingest, and what did the gate do about it?
What's included
- 15-minute local PoC to validate the detection pipeline before you commit
- Controlled deployment of the URL Trust Gate service in your environment
- Integration with one consumer surface: LangChain SDK, LlamaIndex SDK, RASP Python, browser extension, or endpoint agent
- Three reputation feeds optionally enabled: Google Safe Browsing v4, Microsoft SmartScreen, VirusTotal v3
- Policy decisions — allow, warn, redact, sandbox, block, isolate — on every evaluated URL
- Evidence records written to audit service on every non-cached decision
- Bi-weekly pilot review calls and a pilot-close readout for your security leadership
Program 02
Runtime Control + Evidence
Detection, policy enforcement, and decision-level evidence across your AI deployment.
The problem it solves
Prompt injection, credential leaks, sensitive data exposure, and provider misuse are happening inside your AI applications today. Without runtime enforcement and decision-level evidence, you cannot detect them, prove they did not occur, or demonstrate control to auditors.
Pilot outcome
Runtime control over what your AI systems do, with evidence you can show to a CISO, board, regulator, or auditor.
What's included
- URL Trust Gate pilot (as above) plus runtime detection and enforcement
- Prompt injection, sensitive data, toxicity, and output-safety detection on AI requests
- Policy engine: tenant-scoped rules tied to actor, workload, model, provider, and data context
- Agent identity registration and delegation chain tracking for autonomous AI workflows
- Audit service with immutable, attributable evidence records for SOC, audit, and legal review
- Response orchestration: block, redact, notify, ticket, or route on policy violation
- Compliance evidence snapshot against relevant frameworks (NIST AI RMF, SOC 2, ISO 27001, and others)
- Dedicated pilot design partner engagement and quarterly business review
Program 03
Agentic AI Trust Gate
Full trust control for autonomous AI agent workflows.
The problem it solves
Autonomous AI agents act: they fetch URLs, call APIs, read documents, execute tools, and take decisions in production systems. Every action is a trust decision. Without pre-ingestion gating, runtime enforcement, agent identity, and evidence, you have no control over what your agents do or proof that they did not cross a policy boundary.
Pilot outcome
Auditable, evidence-backed control over autonomous AI agent behaviour in regulated production workflows.
What's included
- Everything in Runtime Control + Evidence
- URL Trust Gate on every agent-bound external fetch, document retrieval, and tool-call URL
- Agent identity: registration, tenant scoping, allowed/denied tools, delegation chains, revocation paths
- Policy enforcement on agent-issued API calls, model queries, and tool invocations
- Pre-ingestion filtering of RAG retrieval sources before content enters agent context
- Post-action evidence chain: what the agent saw, what it decided, what it did, what policy said
- Incident response integration: agent suspension, scope reduction, token revocation on anomaly
- Executive-level pilot design and a pilot-close briefing for board or risk committee
The Category We Own
Pre-ingestion trust control for AI agents
and enterprise AI workflows.
Every threat that reaches an AI system after it has been fetched is harder to stop than one that was evaluated before ingestion. CyberArmor enforces that boundary — and records the evidence to prove it.