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Most AI governance platforms govern models you build. That's not where 90% of your risk lives. Strac governs the AI your employees use — ChatGPT, Microsoft Copilot, Claude, Gemini, and 50+ other tools — with real-time content inspection, shadow AI discovery, and policy enforcement that works in minutes, not quarters.
Draft a support reply for Jane Smith (SSN 078-05-1120, card 4532-xxxx-xxxx-6789, MRN-2847291).
The AI governance category is quietly splitting in two. Understanding the split is the difference between an AI governance investment that protects your business and one that ships a beautiful dashboard while your real risk walks out the door.
Organizations are caught between two truths: AI adoption is the single biggest productivity lever of the decade, and AI usage creates data security risk that traditional controls were never designed to stop. A policy document that says "don't paste sensitive data into ChatGPT" is not a control — it's a wish.
Employees use 3–5× more AI tools than IT has sanctioned. Personal ChatGPT Plus, personal Claude, Perplexity — all operating outside every control you've built.
Customer records, source code, financial data, PHI, and credentials flow into AI tools every hour. Samsung banned ChatGPT within weeks because three engineers leaked semiconductor IP.
Copilot surfaces anything a user has permission to read. Stale "Anyone with the link" shares, broad M365 groups, and forgotten guest access become one-prompt-away exposures.
An employee asks Copilot to summarize customer data, then pastes the output into Slack, Gmail, or personal AI tools. Microsoft's governance boundary ends at M365. The risk doesn't.
HIPAA, PCI DSS, GDPR, and SOC 2 were written before LLMs. You still need to prove you're controlling what sensitive data reaches AI — and auditors are starting to ask.
Once AI agents call tools autonomously via MCP, "what did the employee paste" becomes "what did the agent decide to send." Traditional controls don't see it.
Effective AI usage governance isn't one tool — it's three layers working together. Most organizations skip to Layer 2 (prompt DLP) and find they've secured one door while leaving five others open.
How: Endpoint agent discovers local AI apps, MCP servers, and browser-based AI usage. Email enforcement identifies personal-account usage on corporate devices.
Outputs: Shadow AI inventory, usage baseline, policy gap report.
How: Browser extension on ChatGPT, Copilot, Claude, Gemini, and 50+ AI tools. Endpoint DLP for local AI apps. MCP DLP for agentic workflows. Cross-SaaS DLP for the tools that feed AI connectors.
Outputs: Real-time blocks, user education prompts, audit-grade logs.
How: SOC 2 / HIPAA / PCI-aligned evidence, NIST AI RMF control mapping, EU AI Act article alignment, executive dashboards.
Outputs: Audit reports, compliance evidence, board-level AI risk metrics.
Strac's endpoint agent discovers every AI tool in use — sanctioned, unsanctioned, personal, locally-run. Full inventory in 24 hours.
Chrome, Edge, Firefox, Safari extension inspects every prompt in ChatGPT, Copilot, Claude, Gemini, Perplexity, and 50+ AI tools. Block, warn, or audit.
When a user tries to sign up with a personal email, Strac nudges them toward the corporate account — converting shadow AI into governed AI.
PII, PCI, PHI, secrets (API keys, OAuth tokens, credentials), custom patterns. ML-based accuracy, not just regex.
OCR-based detection inside JPEG, PNG, PDF, DOCX, XLSX, ZIP. The only platform that redacts sensitive data inside images before they reach AI.
Scan SharePoint, OneDrive, Teams for "Anyone with the link," "Everyone in org," and stale guests before Copilot surfaces them. Bulk fix in hours.
Auto-discover, auto-label, and reconcile Microsoft Purview sensitivity labels at the item level. Container labels don't inherit — Strac fixes that.
Redact sensitive data in the SaaS tools upstream of AI: Slack, Zendesk, Jira, Salesforce, Google Drive, SharePoint, Box.
Inspect and redact sensitive data at the Model Context Protocol server boundary. Block before agents see it, or redact inline.
Pre-built mapping to NIST AI RMF, EU AI Act, ISO 42001, HIPAA, PCI DSS, SOC 2. Auto-generate audit evidence.
Define policies per team, data type, AI tool. Finance blocked on PCI, healthcare redacts PHI, marketing free to use Claude. All centrally managed.
Every detection, block, warning, and override logged. Native Splunk, Datadog, and SIEM integrations.
An employee pastes a customer record — name, DOB, SSN — into ChatGPT to draft an outreach email. Strac's browser extension detects the SSN before submit. Three modes: Block the prompt, Warn the user with a redacted preview, or Audit silently.
Strac's endpoint agent discovers that 47 users across marketing and engineering are using personal Claude accounts despite your ChatGPT Enterprise rollout. Enterprise email enforcement nudges them toward the corporate Claude integration. Shadow usage drops 80% in two weeks.
Before rolling out Microsoft 365 Copilot, Strac scans your tenant and surfaces 18,000 files shared with "Anyone with the link" — including 2,400 containing PII, PHI, or financial data. Bulk remediation kills the public links in hours.
Your internal AI agent queries a patient-records MCP server via Claude Desktop. Strac's MCP DLP layer intercepts the response and replaces medical record numbers, patient names, and dates-of-birth with safe placeholders before the agent's context is populated.
Strac maps to the frameworks your auditors, board, and regulators care about. Pre-built mapping means audit evidence is generated continuously, not rebuilt from scratch each quarter.
| Framework | Strac Coverage | Primary Controls |
|---|---|---|
| NIST AI RMF | Govern / Map / Measure / Manage — usage governance layer | MEASURE-2.7, MANAGE-2.1, GOVERN-1.5 (data handling, third-party AI, documentation) |
| EU AI Act | Articles 10, 15, 25, 26 — data governance, accuracy, transparency for deployers | Data logging, incident reporting, human oversight for high-risk systems |
| ISO 42001 | Clauses 7.5, 8.2, 8.3 — documented information, risk treatment, operational controls | AI system inventory, usage monitoring, third-party AI assurance |
| HIPAA | §164.308, §164.312 — administrative and technical safeguards for PHI-touching AI | Access control, audit logs, integrity controls on AI interactions |
| PCI DSS 4.0 | Requirements 3, 4, 10 — stored/transmitted cardholder data and logging | Cardholder data inspection in AI prompts, transmission controls, audit trails |
| SOC 2 | Common Criteria CC6 (logical access), CC7 (system operations) | AI tool access controls, usage monitoring, change management |
| Capability | Strac | Credo AI / IBM | Netskope / Zscaler | Nightfall AI | Microsoft Purview |
|---|---|---|---|---|---|
| AI usage governance (employees) | ✓ Core | ✗ | Partial | ✓ | M365 only |
| AI model governance | Not focus | ✓ Core | ✗ | ✗ | ✗ |
| Real-time prompt inspection | ✓ Browser + endpoint | ✗ | CASB proxy | ✓ Browser | Copilot only |
| Shadow AI discovery (endpoint) | ✓ | ✗ | ✗ | Partial | ✗ |
| Copilot oversharing remediation | ✓ | ✗ | ✗ | ✗ | Partial |
| MCP DLP (agentic) | ✓ | ✗ | ✗ | ✗ | ✗ |
| Image / document OCR redaction | ✓ | ✗ | ✗ | ✗ | Limited |
| Agentless SaaS integrations | ✓ 50+ | ✗ | Proxy | Limited | M365 only |
| Deployment time | Under 10 min | Months | Weeks | Days | Weeks |
| TLS break / proxy required | ✗ No | — | ✓ Yes | ✗ No | ✗ No |
Honest positioning: if you're training foundation models and need model registry or bias evaluation, choose Credo AI or IBM watsonx.governance. If you're one of the 95% of enterprises whose AI risk is employees using third-party AI tools, Strac is purpose-built for that.
OAuth-based integration with Microsoft 365, Google Workspace, Slack, Salesforce, and other tools. Agentless. Read-only by default until you approve enforcement.
Chrome Enterprise / Edge Group Policy / Firefox Enterprise push. No user interaction required. Covers ChatGPT, Copilot, Claude, Gemini, and 50+ AI tools immediately.
Jamf, Intune, Kandji, or any standard MDM. Silent install. Enables shadow AI discovery, local AI tool inspection, and MCP DLP.
Start in Audit mode to baseline usage. Move to Warn to educate users. Move to Block for specific data types (PCI, PHI, secrets) based on regulatory needs.
Strac does not intercept TLS. No proxy server to deploy. No network changes. No performance impact on users. Detection runs locally in the browser and on the endpoint — fast, private, and invisible to the user until policy fires.
AI governance is the set of policies, processes, and controls organizations use to manage AI risk. It splits into two subcategories: AI model governance (managing risk in models your company builds — bias, drift, provenance) and AI usage governance (managing risk in how your employees use third-party AI tools — data leaks, shadow AI, prompt injection, regulatory exposure). Strac focuses on AI usage governance, which is where 90%+ of enterprise AI risk actually lives.
Model governance (Credo AI, IBM watsonx.governance, Cranium) manages risk in models your company trains and deploys — essential for the ~5% of enterprises building ML/LLM systems. Usage governance manages risk in how employees use third-party AI tools like ChatGPT, Copilot, and Claude — relevant to essentially every enterprise. They're complementary, not competing, but confusing them leads to the wrong investment.
No. AI governance is broader — policy, process, technical controls, and cultural norms. AI compliance is the subset that proves you meet specific regulatory requirements (NIST AI RMF, EU AI Act, ISO 42001, HIPAA, PCI). Strac delivers both: governance capabilities as a platform and pre-mapped compliance evidence for auditors.
An AI usage governance platform discovers AI tools in use, inspects content flowing into and out of those tools, enforces policy in real time (block, warn, or audit), and generates compliance evidence. Strac does all four across 50+ AI tools, in under 10 minutes of deployment, without a proxy or TLS break.
A written policy helps but isn't a prerequisite. Most organizations discover their written AI policy is wishful thinking once they see actual usage data. Start with Strac in Audit mode (no user impact, full visibility), use the data to write a realistic policy, then turn on enforcement. A policy without detection is unenforceable.
Strac maps to NIST AI RMF, EU AI Act, ISO 42001, HIPAA, PCI DSS 4.0, SOC 2, and GDPR. Pre-built mappings generate audit evidence continuously. Custom mappings are available for HITRUST, FedRAMP, CMMC.
Ten minutes to connect SaaS tools and push the browser extension. Twenty-four hours for full shadow AI discovery baseline. Most customers are in Audit mode within an hour and in Block mode within two weeks after policy review.
Netskope and Zscaler govern AI at the network layer with a TLS-breaking proxy — they cover traffic routed through their cloud. Strac governs at the endpoint and browser — no proxy, no TLS break, works on remote employees, covers BYOD, and sees data before it leaves the browser. Many customers run both: network for malware, Strac for AI.
Yes. Strac covers M365 Copilot (prompts, outputs, and the underlying oversharing that Copilot amplifies), Copilot Studio, Copilot Chat, and Copilot for Sales / Service.
Pricing is per-user, based on modules (SaaS / Cloud / GenAI / Endpoint). A typical mid-market deployment with GenAI + SaaS DLP starts around $30–50 per user per year, with volume discounts. Quote in 15 minutes.
See Strac govern every AI tool your employees use in a 15-minute demo. We'll show you your actual shadow AI usage, real-time prompt DLP in action, and how to ship AI adoption without shipping data risk.