VARC scores behavior, enforces compliance, and produces cryptographic evidence on every AI agent interaction — in real time. Not after the fact. Every single one.
Every AI agent interaction passes through a 4-phase runtime pipeline before delivery or blocking.
Built for the gap between governance platforms that define policy and observability tools that monitor after the fact.
8-dimension BEV scoring on every interaction. Not binary guardrails — a continuous risk surface.
CUSUM algorithm tracks behavioral drift across turns. Catches multi-turn attacks that single-prompt filters miss.
Interactive challenge mode. Prompt injection, social engineering, encoding tricks — test your governance live.
Network scan for unregistered AI endpoints. Find rogue LLM API calls before they become breaches.
Live API with 819K+ cross-framework control mappings. Per-agent, per-domain assessment.
Route through Claude, GPT, or Gemini — every prompt governed through the same pipeline.
Hash-chained evidence trail that governs the governance engine's own decisions. Tamper-evident by design.
Embed governance into GitHub Actions, GitLab CI, Jenkins, Azure DevOps.
ServiceNow AI Control Tower, Palo Alto Networks AIRS/Agentix, and custom REST API.
Every other tool does part of the job. VARC does the part nobody else does.
| Capability | VARC | Governance Platforms | Observability | Prompt Firewalls |
|---|---|---|---|---|
| Per-interaction scoring | 8-dimension BEV | No runtime scoring | Single metrics | Binary pass/fail |
| Graduated response | 5-level GRO | Policy only | Alerts only | Block or allow |
| Session awareness | CUSUM multi-turn | None | Model drift only | Single prompt |
| Agent identity | Cryptographic tokens | None | None | None |
| Evidence chain | Hash-linked audit | Flat logs | Flat logs | None |
| Compliance frameworks | 692 live API | 5-15 packs | None | None |
| Shadow AI discovery | Network scan | Manual inventory | None | None |
Loan processing, claims, customer service AI — governed against lending regulations.
HSA/FSA administration, clinical decision support with full HIPAA enforcement.
Federal AI agents with sovereign deployment and air-gapped configurations.
SOC agents, threat response, code review — governed against security standards.
20 interactive modules. Register a tenant, import agents, run scenarios, assess compliance — all live.
Open Production OpsCenter →Consumption-based pricing. Pay for what you govern.
For teams getting started
For regulated enterprises
For global deployments
Deep expertise in Agentic AI, AI Governance, Cloud Architecture, and Enterprise Data Platforms.
28+ years in enterprise technology. Deep expertise in Agentic AI governance, cloud architecture (GCP certified), AI ethics & compliance strategy, and GSI partnership ecosystems. Built and scaled $245M+ technology portfolios.
Runtime AI governance at scale. Behavioral scoring, policy engines, compliance automation in regulated industries.
Financial services compliance. SR 11-7, ECOA/FCRA, BSA/AML, EU AI Act. GRC platform or Big 4 background.
Python/FastAPI, React, Kubernetes. ServiceNow and PANW integration experience a plus.
Everything you need to integrate, configure, and operate VARC.
Deploy VARC, register your first tenant, and run governed interactions in under 15 minutes.
Deep dive into the 4-phase governance pipeline with architecture diagrams and configuration reference.
Full OpenAPI specification for all 125+ endpoints. API reference available under NDA.
Framework-specific guides for configuring compliance assessment.
Connect VARC to your existing stack with step-by-step connector guides.
Deployment architecture, sovereign configuration, data residency, and encryption.
Technical deep dives, compliance guidance, and market analysis.
Block-or-allow is not governance. Here is why proportional enforcement fundamentally changes enterprise AI risk management.
Every AI security product on the market today operates on the same assumption: a prompt is either safe or dangerous. Pass or fail. Allow or block. This binary model worked tolerably when AI interactions were simple — one question, one answer.
That world no longer exists. Agentic AI systems process chains of interactions over extended sessions. An agent might handle 30 turns in a single claims workflow, each building on context from the last. The attack surface is not a single prompt — it is the behavioral trajectory across the entire session.
The most sophisticated adversarial pattern in production is what we call the sleeping giant. An attacker sends 10 completely benign prompts. Each passes every guardrail. The context window fills with trusted history. Then prompt 11 arrives — leveraging accumulated trust to extract data, escalate privileges, or trigger unauthorized actions.
No single-prompt filter catches this. The 11th prompt, evaluated in isolation, may look only moderately suspicious. It is the behavioral trajectory that reveals the attack — and binary guardrails have no concept of trajectory.
VARC operates on 5 levels, not 2. When behavioral scoring detects early drift indicators — even subtle ones — the system escalates proportionally:
Level 0 — Autonomous: Agent operates freely. Clean behavioral profile.
Level 1 — Monitor: Behavioral indicators shifted. Logging verbosity increases. Operations team notified.
Level 2 — Human-in-the-Loop: Risk crossed a threshold. Decisions queue for human review before delivery.
Level 3 — Restrict: Agent capabilities narrowed. High-risk actions disabled.
Level 4 — Suspend: Full agent suspension. Forensic capture triggered. The emergency stop — but the last resort.
In financial services, a false positive block on a lending agent means a legitimate borrower does not get their loan decision. In healthcare, blocking a medical records query means a provider cannot access patient information during a critical decision. Graduated response gives enterprises proportional risk management with full documentation. That is governance. Binary pass/fail is just a filter.
Shadow AI is the new shadow IT — except the data leakage vector is 100x worse.
In every enterprise network scan we have conducted, between 3 and 12 unregistered AI API endpoints are actively processing corporate data without governance oversight. Marketing pipes customer data through GPT. Engineering runs self-hosted Ollama for code review. Data science calls Anthropic from personal accounts.
Commercial LLM APIs (3-5 endpoints): api.openai.com, api.anthropic.com, generativelanguage.googleapis.com. Employees using AI through browser extensions, personal API keys, or unauthorized SaaS tools.
Self-hosted models (1-3 endpoints): Ollama on port 11434, vLLM instances, HuggingFace inference servers. Often zero access controls.
The PII Exposure Problem: Over 60% of shadow AI endpoints process personally identifiable information — customer names, account numbers, medical records sent to AI systems with no retention policies or compliance framework.
1. Scan your network. VARC detects calls to 12 AI provider signatures including OpenAI, Anthropic, Google, Azure AI, HuggingFace, Cohere, Replicate, and self-hosted models.
2. Classify and risk-score. Marketing using GPT for blog drafts differs from engineering running an unprotected inference server with production database access.
3. Bring under governance or block. For each endpoint: onboard into your framework, block at the network level, or escalate to your CISO.
The OCC guidance was written for traditional models. Here is how to apply it to autonomous AI agents.
OCC Bulletin 2011-12 (SR 11-7) remains the foundational model risk management guidance for US banks. Written in 2011 when models meant logistic regression for credit scoring. It never anticipated autonomous AI agents making real-time decisions.
1. Model Validation (Section III): You cannot run a traditional test dataset against an AI agent responding to free-text prompts in real time. The VARC approach: continuous behavioral validation. Every interaction scored across 8 dimensions. Drift triggers alerts. Quarterly Red Team assessments simulate adversarial scenarios.
2. Ongoing Monitoring (Section IV): For AI agents, outcomes happen in milliseconds across thousands of interaction types. VARC's CUSUM drift detection monitors behavioral patterns per session, flagging deviations from the established envelope.
3. Model Inventory (Section V): For AI agents, this means tracking every model version, data source, and decision pathway. VARC's AI Registry auto-discovers and catalogs agents, maps them to frameworks, and tracks governance coverage. Shadow AI Discovery ensures completeness.
Examiners ask three questions: (1) How do you validate the agent performs as intended? (2) How do you detect behavioral drift? (3) Can you show the evidence trail for a specific decision? VARC provides concrete, auditable answers from the runtime governance pipeline.
Request a demo, explore partnerships, or discuss how VARC fits your requirements.
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Frisco, Texas
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