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.
A measured outcome, not a demo
A pilot-close readout gives your leadership a measured answer: detection rates, false-positive rates, latency impact, and evidence completeness.
One platform.
Four starting scopes.
Every scope deploys the same platform — the same inbound gate, the same policy engine, the same evidence chain — pointed at a different protected surface. Start where the exposure hurts most; widen the boundary when ready.
Typical pilots run 30–90 days depending on scope. Pilots are fixed-scope engagements; pricing is agreed before kickoff.
Starting scope 01
Inbound content & agent context
Protection for everything your AI reads — URLs, documents, retrieval sources.
The full loop on this surface
Hostile content is screened before ingestion, the policy engine decides allow, warn, redact, sandbox, block, or isolate, only sanitized content reaches your models — and every decision lands in the same evidence chain as every other control point.
The problem it solves
Your AI systems fetch, ingest, and act on external URLs and documents. Hidden prompt injection, CSS-concealed instructions, and zero-width-encoded payloads are invisible to existing filters — but read verbatim by LLMs. This scope puts the platform's inbound 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 platform's URL & Context Trust Gate 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
Same deliverable, every scope: a pilot-close evidence review mapped to your frameworks — 17 compliance packs, including SEC, FINRA, and NYDFS 500.
Starting scope 02
Runtime & output enforcement
Policy enforcement on what your AI does and what it sends back.
The full loop on this surface
Inbound gating stays on, detection inspects prompts and responses in flight, policy enforces in both directions — input and output — and every enforcement action writes an attributable record to the same evidence chain.
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
- Everything in the inbound content & agent context scope, plus runtime detection and enforcement
- Prompt injection, sensitive data, and toxicity detection on AI requests and responses
- 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 attributable evidence records for SOC, audit, and legal review
- Response orchestration on policy violation: block, redact, or route
- Compliance evidence snapshot across 17 framework policy packs, including SEC Cyber, FINRA Cyber, NYDFS 500, NIST AI RMF, SOC 2, and ISO 27001
- Dedicated design-partner engagement and a pilot-close readout for security leadership
Same deliverable, every scope: a pilot-close evidence review mapped to your frameworks — 17 compliance packs, including SEC, FINRA, and NYDFS 500.
Starting scope 03
Agentic workflows & tool use
Trust control for autonomous agents that fetch, call, and act.
The full loop on this surface
Every agent-bound fetch and retrieval is gated on the way in, every tool call and model query passes the policy engine under a registered agent identity, actions are enforced on the way out — and the same evidence chain records what the agent saw, decided, and did.
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 the runtime & output enforcement scope
- Inbound trust gating 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
Same deliverable, every scope: a pilot-close evidence review mapped to your frameworks — 17 compliance packs, including SEC, FINRA, and NYDFS 500.
Starting scope 04
Endpoint estate + SIEM
AI security across your fleet, forwarding into the SOC you already run.
The full loop on this surface
The same inbound gate screens what endpoint AI tools reach, the same policy engine drives discovery, redaction, and patch remediation, and every endpoint event forwards to your SIEM and lands in the same evidence chain — per tenant, audit-ready.
The problem it solves
Your employees use AI tools on their laptops today — most of it invisible to your security stack, running on software nobody has patched, with nothing written down for your regulator. This scope puts endpoint agents on Windows, macOS, and Linux, discovers the shadow AI in use, remediates the unpatched software underneath it, and forwards every event to the SOC that watches your fleet.
Pilot outcome
A managed, evidence-backed answer to what AI your endpoints touch, what got patched, and what your regulator can see.
What's included
- Endpoint agents deployed on Windows, macOS, and Linux — the Windows install path hand-verified on real hardware
- Shadow AI discovery: an inventory of the AI tools and providers your endpoints actually reach
- Patch remediation through winget, Homebrew, apt, and yum/dnf — maintenance windows, approval workflow, per-app auto-approve
- Software-update inventory: every endpoint reports upgradable packages with current and available versions
- SIEM forwarding of endpoint and control-plane events into the operating SOC — Splunk, Sentinel, QRadar, Elastic, Google SecOps, or Syslog/CEF
- Compliance evidence mapped across 17 framework packs — SEC Cyber, FINRA Cyber, and NYDFS 500 first — persisted per tenant, ready for audit
Same deliverable, every scope: a pilot-close evidence review mapped to your frameworks — 17 compliance packs, including SEC, FINRA, and NYDFS 500.
Where CyberArmor Stands
AI security you can prove —
before you commit to production.
The independent AI-security platform for regulated enterprises — enforcement in both directions at every control point, evidence mapped to 17 compliance frameworks including SEC, FINRA, and NYDFS 500, honest about what's production, and provable on your own laptop in 15 minutes.