AI conversations often jump straight to models: which one, how accurate, how fast.

Boards should start somewhere else: the data.

In December 2025, Reuters reported that Blackstone was leading a $400 million investment in data security firm Cyera at a $9 billion valuation (per a Wall Street Journal report). (Reuters) Whether you follow funding markets or not, the signal is clear: “data security” is becoming the foundation for how companies adopt AI responsibly.

Why? Because AI doesn’t just process data—you connect it to data.

The real risk surface in AI projects: connectivity

Most AI risk in modern companies isn’t about a model “going rogue.” It’s about everyday decisions that expand access:

• Connecting copilots to internal docs, tickets, chat logs, and repos

• Granting broad permissions so teams can “move fast”

• Using third-party AI tools with unclear retention, logging, or training policies

• Duplicating data into new pipelines for experimentation

• Losing track of where sensitive data actually lives

If leadership can’t answer “what sensitive data we have, where it is, and who can access it,” AI will amplify uncertainty—fast.

A board-ready way to define “data security for AI”

Keep it practical. Data security for AI is the ability to:

1. Discover sensitive data across systems (including shadow IT and SaaS sprawl)

2. Classify what matters (customer data, regulated data, source code, trade secrets)

3. Control access (least privilege, strong identity, reviewed privileges)

4. Constrain movement (egress controls, DLP where it’s justified, logging)

5. Prove governance (evidence you can show auditors, investors, and customers)

This isn’t a “big bang” program. It’s a staged discipline.

The Data Exposure Triage: a 30-day plan that creates clarity

If you want results quickly, run a triage that produces a decision memo.

Step 1: Create a data map you can defend

Pick 10–15 systems where sensitive data is most likely to live: CRM, support tickets, analytics, data warehouse, cloud storage, collaboration tools, source control, CI/CD artifacts, HR and finance systems. The goal isn’t perfection. The goal is a credible first map.

Step 2: Identify your “crown jewels” and the paths to them

For each crown-jewel dataset, document: who owns it (business owner, not just IT), which roles can access it, which integrations replicate it, and which vendors can touch it.

Step 3: Clean up “everyone can read everything” access

The most common exposure in growth-stage companies is overbroad access that made sense at 30 people and is dangerous at 300.

Quick wins: remove stale accounts and unused API tokens; tighten admin and privileged roles; require MFA for privileged access (and ideally for everyone); enforce SSO for critical SaaS where possible.

Step 4: Decide what not to connect to AI (yet)

If you don’t have visibility and controls, don’t connect AI tools to raw customer data, incident response artifacts, internal legal/HR content, or production secrets and keys. Connect AI to curated datasets and knowledge bases first.

What good looks like in 90 days

A realistic posture doesn’t require a giant platform rollout. It requires focus:

• A maintained AI Use Register listing each AI use case, data inputs, and owners

• Clear sensitive data categories with handling rules

• A privileged access review cadence (monthly/quarterly)

• Vendor due diligence that asks AI-specific questions (retention, training, logging)

• Logging that supports investigations and customer questions

And most importantly, leadership can answer investor and customer questionnaires without scrambling.

The board questions I’d ask this quarter

1. Where is our most sensitive data, and how confident are we?

2. Which AI tools and integrations have access to it today?

3. What would we do if data appeared in an AI output or was shared externally?

4. What evidence can we show that access is controlled and reviewed?

5. What’s our plan to reduce uncertainty in 30 and 90 days?

If you’re adopting AI and want a clear, defensible data security posture, vCSO.ai can run an AI Data Exposure Diagnostic and deliver a prioritized control plan (30/90-day roadmap).