AI Security & Governance
Your employees are already using AI — ChatGPT, Claude, Copilot, browser extensions, plugins. Most of it happens outside your visibility, and sensitive data is walking out the door with it. We help you see it, govern it, and enable it safely.
Key Capabilities
- Shadow AI discovery across browser, SaaS, and endpoint
- OAuth-granted AI app inventory and risk scoring
- Prompt-level DLP and data classification alignment
- Acceptable Use Policy for generative AI and LLMs
- Approved-tool catalog with enterprise alternatives
- OWASP LLM Top 10 assessment for customer-facing AI
- Prompt injection and jailbreak red teaming
- AI agent permission and tool-use review
- NIST AI RMF alignment and gap analysis
- Vendor AI feature risk reviews (Copilot, Gemini, etc.)
- Employee AI literacy and safe-use training
- Board-level AI risk reporting
Overview
AI adoption inside enterprises has outpaced security by a wide margin. Employees are pasting source code, customer PII, financial data, M&A documents, and internal strategy into public LLMs every day. Browser extensions with AI capabilities read every page they load. Shadow AI tools get connected to corporate email, calendar, and cloud drives via OAuth without IT ever seeing the consent screen. The average Fortune 500 has hundreds of AI-enabled SaaS apps in active use — most unknown to security. This isn't a theoretical risk. We have seen confidential board decks surface in chatbot training incidents, regulated health data exfiltrated through "helpful" summarization extensions, and production credentials leaked through auto-complete plugins. Traditional DLP and CASB tools were not designed for prompt-level inspection or for the AI supply chain. And blanket bans do not work — they push usage underground and destroy the productivity advantage your competitors are already capturing. Our AI Security & Governance practice gives you a defensible middle path. We start with discovery: a complete inventory of AI tools in active use across your environment — browser extensions, SaaS integrations, API keys, personal accounts on corporate devices, and LLM-powered features hiding inside tools you already own. Then we build governance you can actually operationalize: acceptable use policies tied to data classification, DLP rules tuned for prompt content, approved-tool catalogs with business-justified sanctioning, and enterprise-grade alternatives that give employees the productivity they want without the data egress risk. We also harden the AI systems you build. If you are shipping LLM features to customers, we assess prompt injection exposure, data poisoning risk, model supply chain integrity, agent tool-use permissions, and the OWASP LLM Top 10 against your architecture. For regulated industries, we map AI use to HIPAA, GLBA, PCI-DSS, SOC 2, and the NIST AI RMF so your program holds up to audit. And because the landscape shifts weekly, every engagement includes continuous monitoring recommendations and a governance cadence designed to keep pace.
What We Deliver
Tangible outcomes and deliverables from our engagement.
Shadow AI Inventory Report
Complete view of AI tools in use across your environment — sanctioned, unsanctioned, and OAuth-connected — with risk scoring and data exposure analysis.
AI Acceptable Use Policy
Enforceable policy tied to your data classification model, covering generative AI, coding assistants, browser extensions, and personal-account use on corporate devices.
Approved Tool Catalog
Curated list of sanctioned AI tools with business justifications, data handling requirements, and per-tool usage guidelines employees can actually follow.
Prompt DLP Ruleset
Tuned data loss prevention rules for prompt content — tailored to your regulated data types and integrated with existing DLP/CASB infrastructure.
OWASP LLM Top 10 Assessment
Technical security review of customer-facing LLM features covering prompt injection, insecure output handling, training data poisoning, and agent permissions.
AI Red Team Report
Adversarial testing results with reproducible prompt injection, jailbreak, and data exfiltration findings — plus remediation guidance.
NIST AI RMF Gap Analysis
Alignment assessment against the NIST AI Risk Management Framework with a prioritized roadmap to close governance gaps.
Executive AI Risk Briefing
Board-ready briefing quantifying AI exposure, business impact scenarios, and investment-prioritized mitigations.
Employee AI Safe-Use Training
Role-tailored training modules for engineering, legal, finance, HR, and executive staff on safe AI usage and red flags to watch for.
Our Process
A proven methodology that delivers results — scroll to walk it.
STEP 01 / 04
Discovery & Shadow AI Mapping
We scan your environment — browser telemetry, SaaS OAuth grants, network traffic, endpoint inventory — to surface every AI tool actually in use, including the ones IT does not know about.
Ideal For
- Enterprises with no visibility into employee AI tool usage
- Regulated organizations (healthcare, finance, legal) facing AI compliance questions
- Companies shipping LLM-powered features to customers
- Security teams being asked to 'allow AI' without a framework
- Organizations that have banned AI and watched usage go underground
- Boards demanding an AI risk position before the next audit
- Teams preparing for NIST AI RMF, EU AI Act, or state AI regulations
What to expect
Three engagement shapes most clients pick from. We scope and fixed-bid before signature — no open-ended T&M.
AI Inventory & Risk Snapshot
2–3 week fixed-bidClinics or healthcare-tech firms that have started adopting AI scribes, ambient documentation tools, or AI-assisted coding — and need a defensible answer for the board, the cyber-insurance carrier, or the next OCR investigator who asks where PHI is going.
Shadow-AI discovery across the practice, data egress and vendor-trust analysis for each tool, BAA gap report for AI vendors, and a 90-day remediation roadmap. The output is a board-presentable picture of AI risk grounded in the NIST AI RMF Generative AI Profile (NIST AI 600-1).
Included
- Shadow-AI discovery across browser, OS, and SaaS surfaces
- Per-tool data egress + vendor-trust assessment
- BAA gap analysis for every AI vendor touching ePHI
- Executive briefing deck (board / leadership-ready)
- 90-day remediation roadmap with owners and dates
Not included (scoped separately)
- Ongoing governance (covered by the program engagement below)
- LLM red-teaming for AI features you ship to your patients (separate engagement)
- Hands-on tool deployment / DLP rule writing (scoped separately)
Healthcare AI Governance Program
4–6 week build · optional ongoing retainerMulti-site clinic groups, hospitals, or healthcare-tech firms standing up a formal AI program — typically when an EHR vendor enables AI features at scale, when an ambient-documentation rollout is going company-wide, or when a board has asked for an AI-risk policy.
End-to-end governance build: written AI Acceptable Use Policy mapped to the NIST AI RMF and HIPAA Security Rule, approved-tool catalog (so clinical staff don't route around the policy), DLP rules for prompt content, training rollout, and a quarterly governance cadence to keep the program honest as the landscape changes.
Included
- Everything in the AI Inventory & Risk Snapshot
- Written AI Acceptable Use Policy (clinical-staff-readable)
- Approved-tool catalog with risk tiers and BAA status
- DLP rule set for prompt content (Microsoft Purview / equivalent)
- Staff training rollout (clinicians + administrative + IT)
- NIST AI RMF Generative AI Profile (AI 600-1) mapping
- Quarterly governance cadence template (continued under retainer if desired)
Not included (scoped separately)
- Patient-facing AI feature security (LLM Product Security engagement)
- Tool vendor selection / procurement (advisory only — not a buyer)
LLM Product Security Review
3–6 week fixed-bidHealthcare-tech firms shipping LLM-backed features to customers, EHR add-on vendors integrating GenAI, and digital-health platforms whose patients interact with AI directly. Pre-launch security review or post-launch hardening.
Deep technical assessment grounded in the OWASP LLM Top 10 (2025) and MITRE ATLAS. We exercise prompt injection, data exfiltration, agent-permission scope creep, and PHI-leakage scenarios specific to your architecture.
Included
- Architecture review (LLM, retrieval layer, agent permissions, tool calling)
- Prompt-injection red-team across documented attack patterns
- PHI-leakage testing (synthetic patient-record probes)
- Agent-permission scope-creep analysis
- Written report with severity-rated findings + remediation
- 1 retest pass on critical findings after remediation
Not included (scoped separately)
- Generic web-app penetration testing (refer-out, partner-delivered)
- Source-code-level secure code review (separate engagement)
- Continuous LLM monitoring / runtime defense (advisory only)
Each engagement is fixed-bid against a written scope. We publish methodology, not pricing — every quote is custom to your environment, regulated obligations, and timeline.
Get a custom quoteNot sure which shape fits? Take the 2-minute assessment — eight questions, intent-tailored next step, no calendar required.
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Common questions
Straight answers to what prospective clients ask us most about ai security & governance.
We just use AI scribes and ChatGPT — do we actually have AI security risk?
That's exactly the profile where risk hides. An ambient scribe listens to every patient encounter; whatever it captures is protected health information flowing to a vendor's inference endpoint. The questions that matter: is there a BAA covering the AI processing specifically, where are audio and transcripts retained, and can staff paste PHI into consumer tools with no agreement at all? Most practices can't answer all three — the snapshot engagement exists to change that.
What does the AI Inventory & Risk Snapshot actually produce?
In two to three weeks, fixed-bid: a shadow-AI inventory across browser, OS, and SaaS surfaces; a per-tool data-egress and vendor-trust assessment; a BAA gap report for every AI vendor touching ePHI; a board-ready executive briefing; and a 90-day remediation roadmap with owners and dates. The framing is grounded in the NIST AI RMF Generative AI Profile (NIST AI 600-1), so the output stands on a named framework rather than our opinion.
Is there a regulation that specifically requires AI security?
There is no single AI security statute today, but that's the wrong comfort. HIPAA applies in full whenever PHI moves through an AI tool — the Security Rule doesn't care that the processor is a model. NIST's AI Risk Management Framework is the structure regulators, insurers, and boards increasingly expect to see, and the EU AI Act applies if you serve European users. We build and cite against those sources in every deliverable.
Do you test the AI systems themselves, or just write policy?
Both, honestly scoped. For AI you ship to customers, the LLM Product Security Review exercises prompt injection, PHI-leakage, and agent-permission scope creep against your real architecture, grounded in the OWASP LLM Top 10 and MITRE ATLAS. For AI you consume, we assess data flows, configuration, and vendor posture, and build the governance that keeps the picture current. What we don't do is retrain or fix a vendor's model — where a finding belongs to the vendor, we document it and arm you for that conversation.
Our EHR vendor turned on AI features. Isn't that their problem?
Partly — and the part that remains yours is the part an OCR investigator would ask about. The vendor owns their model's security; you own the BAA language, the configuration choices, which workflows feed it data, and whether your risk analysis accounts for it. 'The vendor said it's fine' has never been a defensible control. We help you verify instead of assume.
Book a 30-min discovery call
Tell us about your environment and the outcome you need. No slide decks, no sales pressure — just a conversation about whether ai security & governance is the right next step.
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