Your employees are already using AI. The question isn't whether your organization will adopt AI tools — it's whether you understand what data is leaving your environment, who has access to it, and what compliance obligations you're triggering without knowing it.
ChatGPT, Microsoft Copilot, Google Gemini, Claude, Perplexity — these tools are already inside your organization. Your employees are using them to draft emails, summarize reports, analyze financial data, generate code, and process customer information. Most of them started using AI tools before anyone in leadership approved it, configured it, or set a single policy around it.
This is called shadow AI — the unauthorized adoption of AI tools across your workforce. It's not a hypothetical risk. It's happening right now in every industry, at every company size. The data your team pastes into an AI prompt doesn't stay in the chat window. Depending on the tool, the provider, and the configuration, that data may be stored, logged, used for model training, or accessible to the provider's engineering team.
"The biggest AI security risk in your organization isn't a sophisticated attack — it's your own team copying and pasting sensitive data into tools you haven't vetted, haven't configured, and haven't set policies for."
AI and LLM tools introduce a category of risk that doesn't map cleanly to traditional cybersecurity frameworks. These aren't just technology risks — they're data governance, compliance, operational, and legal risks.
Every time an employee pastes client data, financial figures, source code, or internal communications into an AI tool, that data leaves your environment. Depending on the provider's terms of service, it may be retained, logged, or used to train future models.
Data LossEmployees adopt AI tools faster than IT can evaluate them. Free-tier AI services, browser extensions, third-party plugins, and personal accounts used for work all create unmonitored data channels that bypass your existing security controls.
Governance GapIf an employee pastes patient records into ChatGPT, that's a potential HIPAA violation. Credit card data triggers PCI DSS. Student records trigger FERPA. AI tools don't have compliance awareness — your organization bears the regulatory responsibility.
Regulatory RiskIf your organization builds customer-facing AI features — chatbots, automated support, AI-powered search — those systems can be manipulated through prompt injection. Attackers craft inputs that override the AI's instructions to extract sensitive data.
Application RiskYour SaaS vendors are embedding AI into their products — often without notifying you. Your CRM, HR platform, accounting software, and email provider may already be processing your data through AI models with their own retention policies.
Vendor RiskProprietary business strategies, trade secrets, product roadmaps, and client lists pasted into AI tools may lose their trade secret protection. If data enters a model's training set, it can surface to competitors using the same service.
IP & LegalIf your organization uses technology — and especially if your employees have internet access — you have AI exposure. These risks are amplified in regulated industries where data handling obligations are explicit and penalties for violations are severe.
Providers using AI for clinical notes, patient communication, or billing summaries risk HIPAA violations every time protected health information enters a third-party AI system.
Banks, credit unions, and fintech firms face GLBA, NYDFS, and SOX obligations around data handling. AI tools used for financial analysis or customer communications create audit trail gaps.
Attorneys using AI to draft briefs, review contracts, or summarize depositions are placing privileged client information into third-party systems. The liability exposure is already real.
Schools and childcare organizations handling student records are bound by FERPA and COPPA. AI tools used for grading or communication can inadvertently process minor data through non-compliant systems.
PCI DSS governs cardholder data environments. AI-powered customer service tools, loyalty programs, and analytics platforms that touch payment data create new vectors for exposure.
Industrial operations increasingly rely on AI for supply chain optimization and predictive maintenance. Proprietary production data and vendor contracts shared with AI tools represent competitive intelligence risk.
We don't sell AI tools. We don't implement AI platforms. We don't have vendor partnerships that bias our recommendations. We assess your actual AI exposure and give you a clear, actionable plan to address it. Our approach applies the C.V.I.P²-A methodology, adapted specifically for AI and LLM risk.
We identify every AI tool, LLM integration, browser extension, and AI-powered SaaS application in use across your organization — including the ones IT doesn't know about. Employee surveys, network traffic analysis, SaaS audit reviews, and browser extension inventories.
For each identified AI tool, we analyze what data is being sent, how it's processed, what the vendor's retention and training policies are, and whether data flows comply with your regulatory obligations. Real exposure, not theoretical risk.
We evaluate your existing acceptable use policies, data classification schemes, and security awareness training against the reality of AI adoption in your workforce. Most organizations either have no AI policy, or have one too vague to enforce.
We map your AI usage against applicable frameworks — HIPAA, PCI DSS, FERPA, COPPA, GLBA, NYDFS, CCPA, GDPR — and identify specific compliance gaps. We tell you exactly which regulations you're at risk of violating and how.
Leadership receives a clear, non-technical executive briefing on AI risk exposure. Your technical team receives a prioritized remediation plan with specific policy templates, approved tool lists, configuration recommendations, and training curriculum. Both audiences get what they need to act immediately.
Every AI Security Assessment engagement delivers a comprehensive package designed for two audiences: leadership who need to understand strategic risk, and technical teams who need to implement solutions.
AI adoption doesn't create new compliance requirements — it creates new ways to violate existing ones. Every regulation that governs how you handle sensitive data applies equally to data processed through AI tools.
Protected health information in AI prompts
Cardholder data environment AI exposure
Student and children's data in AI systems
Financial services AI governance
California consumer data and AI processing
AI risk within cybersecurity frameworks
Financial data protection and AI tools
International data transfer through AI
This isn't a problem you can defer to next quarter's budget cycle. The exposure is accumulating daily, and the regulatory landscape is tightening. Every week your organization operates without AI governance, the risk compounds.
The EU AI Act is now enforceable. US state-level AI legislation is accelerating. HIPAA and PCI DSS auditors are beginning to ask about AI data handling. Compliance frameworks haven't caught up yet — but enforcement is ahead of policy, and your organization needs documentation in place before the audit happens.
Every day without an AI acceptable use policy is another day your employees are sending proprietary data, client information, and regulated records into third-party AI tools. The exposure happened yesterday. The question is whether you know the scope of it yet.
New AI tools launch weekly. Your SaaS vendors are embedding AI features into existing products without notification. The shadow AI surface inside your organization is growing faster than any other threat vector, and it's completely invisible without a deliberate assessment.
Organizations that adopt AI safely — with governance, visibility, and compliance built in — gain a competitive advantage. They move faster because they have guardrails. Organizations without AI governance will either restrict adoption entirely or face incidents that restrict it for them.
"Organizations don't fail because they ignored AI security — they fail because they didn't know where they were actually exposed. Our job is to show you exactly where that is, in language your entire leadership team can understand and act on."
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