Private AI or Public Models? A CEO's AI Governance Playbook

In this article:

• The Enterprise AI Governance Crisis Hiding in Plain Sight
• What Every CEO Needs to Know About AI Deployment Choices
• The Hidden Risks of Shadow AI
• Understanding Private vs Public AI Models
• The NIST Framework: Your Roadmap for AI Risk Management
• Regulatory Compliance: The Non-Negotiables
• Building Your AI Governance Decision Framework
• Measuring Success: Controls and Metrics That Matter
• Your Next Steps: Partner with Sentry for AI Governance

The Enterprise AI Governance Crisis Hiding in Plain Sight

Last week, a financial services executive discovered that sensitive client data had been uploaded to ChatGPT by a well-meaning analyst trying to draft a compliance report faster. The exposure wasn't caught by any security tool. The executive only found out when the analyst mentioned it in a team meeting. This scenario plays out in businesses every single day, and here's the uncomfortable truth: it's probably happening in your organization right now and you might not even know.

Between 2023 and 2024, corporate data pasted or uploaded into AI tools rose by a staggering 485%. The share of sensitive data in these uploads nearly tripled from 10.7% to 27.4%.¹ While 71% of firms now use generative AI in at least one business function, only 24% of those projects include proper security measures.²

You're facing a decision that will define your competitive position for the next decade: how do you harness AI's transformative power without putting your data, your customers, and your business at risk? The choice between private AI deployment and public AI models isn't just a technology decision. It's a strategic imperative that impacts security, compliance, cost, and competitive advantage.

What Every CEO Needs to Know About AI Deployment Choices

When your team asks for AI tools to boost productivity, you're not just deciding which product to buy. You're choosing between fundamentally different approaches to how your most valuable asset—your data—interacts with artificial intelligence.

Public AI Models: Speed vs. Control

Public AI platforms like ChatGPT, Gemini, and standard Microsoft Copilot operate on shared infrastructure. They offer immediate access, powerful capabilities, and no upfront hardware costs. They're the reason 76% of organizations now purchase rather than build AI solutions.³

When your employees paste data into these tools, that information travels to external servers. Even with enterprise agreements and opt-out settings, 95% of enterprises identify cloud security as a key concern.⁴ Your trade secrets, customer information, and proprietary strategies become inputs to systems you don't control.

Private AI Models: Control vs. Complexity

Private AI deployment means your models, your data, and your infrastructure stay within your security perimeter. Healthcare providers use private AI to automate clinical decisions while keeping patient records completely secure. Financial institutions process sensitive transactions without data ever leaving their networks.

The benefits are clear: complete data sovereignty, customization to your specific workflows, and compliance with regulations like HIPAA and GDPR built in from day one. The challenge? Private AI requires investment in infrastructure, expertise, and ongoing management.

The Hybrid Reality Most Businesses Actually Need

Here's what Sentry has learned working with businesses: the answer isn't choosing one or the other. It's building a governance framework that allows you to use both strategically. Low-risk tasks can leverage public AI (with proper deployment) for speed and cost efficiency. High-value, sensitive operations run on private infrastructure where you maintain complete control.

The Hidden Risks of Shadow AI

While you're evaluating deployment options, your employees have already made their choice. They're using AI. The question is whether you know about it.

Shadow AI occurs when employees use unapproved AI tools for work. It's not malicious—it's your team seeking efficiency. But the consequences can be severe. Eighty-eight percent of employees now use personal cloud apps monthly, and 26% upload or send corporate data through them.⁵

Gartner projects that over 40% of AI-related data breaches by 2027 will stem from unapproved or improper generative AI use.⁶ That means nearly half of future security incidents won't come from sophisticated hackers. They'll come from employees who don't realize they're creating risk.

The solution isn't banning AI tools—that's both impossible and counterproductive. The solution is governance: clear policies, approved tools, employee training, and monitoring that gives you visibility without stifling innovation.

Understanding Private vs Public AI Models

Let's break down the practical differences that matter to your business:

The right choice depends on your specific use case. Processing public information to generate marketing ideas? Public AI makes sense. Analyzing proprietary customer behavior patterns to optimize pricing? That belongs on private infrastructure.

The NIST Framework: Your Roadmap for AI Risk Management

When you're building AI governance, you don't have to start from scratch. The National Institute of Standards and Technology's AI Risk Management Framework provides a battle-tested structure that 79% of large enterprises now follow.⁷

The framework organizes AI governance into four core functions:

GOVERN: Building the Foundation

This establishes your AI governance culture. It means defining roles and responsibilities, creating policies that align with legal and regulatory requirements, and ensuring board-level oversight. In Sentry's experience, governance succeeds when it's championed by senior leadership and integrated into existing enterprise risk management—not treated as a separate IT initiative.

Key actions include:

• Assigning clear AI risk ownership at the executive level
• Establishing an AI governance committee with cross-functional representation
• Creating policies for approved AI tools and acceptable use
• Defining consequences for non-compliance

MAP: Understanding Your AI Landscape

You can't manage risks you don't know exist. The MAP function means building a comprehensive inventory of all AI systems in use—both approved and shadow AI. It requires understanding the context: what data each system touches, who uses it, and what business processes depend on it.

Practical steps:

• Create an AI Bill of Materials documenting all models, data sources, and vendors
• Identify high-risk use cases involving sensitive data or critical decisions
• Map data flows to understand where information goes
• Assess third-party AI usage and vendor risk

MEASURE: Implementing Continuous Monitoring

AI systems aren't set-and-forget. They need ongoing evaluation to ensure they're performing as expected and not introducing new risks. This function establishes metrics, testing protocols, and monitoring systems.

Essential metrics include:

• Data loss prevention (DLP) matches on AI inputs/outputs
• Percentage of prompts reviewed before processing
• Adoption rates of approved tools vs. shadow AI
• Incident reports and near-misses
• Bias and accuracy measurements for high-stakes decisions

MANAGE: Responding and Improving

When issues arise—and they will—you need clear procedures for response. This function covers incident management, mitigation strategies, and continuous improvement based on lessons learned.

Implementation includes:

• Automated guardrails that block sensitive data uploads
• Human-in-the-loop review for high-risk prompts
• Rapid response procedures for security incidents
• Regular policy updates based on evolving risks

Regulatory Compliance: The Non-Negotiables

AI governance isn't just good practice—increasingly, it's the law. Regulations vary by industry and geography, but several key frameworks demand attention:

EU AI Act: The Global Standard

The European Union's AI Act, which became enforceable in August 2025, represents the world's first comprehensive AI regulation. Even if you're not in Europe, this matters. The law's extraterritorial reach means any company serving EU customers must comply.

Key requirements:

• Risk-based categorization of AI systems (prohibited, high-risk, limited risk, minimal risk)
• Transparency obligations for all general-purpose AI models
• Documentation of training data and copyright compliance
• Mandatory conformity assessments for high-risk systems
• Fines up to €35 million or 7% of global turnover⁸

HIPAA: Healthcare's Data Shield

Healthcare organizations face strict requirements under the Health Insurance Portability and Accountability Act. Using public AI with protected health information (PHI) creates immediate violations unless specific safeguards are in place.

HIPAA requirements for AI:

• Business Associate Agreements with any AI vendor processing PHI
• Encryption of data in transit and at rest
• Access controls and audit logs
• Risk assessments before deployment
• Breach notification procedures

Many healthcare providers opt for private AI deployment to avoid the complexity of ensuring HIPAA compliance with third-party AI services.

PCI DSS: Protecting Payment Data

Organizations handling credit card information must comply with Payment Card Industry Data Security Standard. Using public AI tools to process payment card data violates PCI DSS unless the AI provider is also PCI compliant and proper safeguards are implemented.

FTC Safeguards Rule: Financial Services Protection

Financial institutions must implement comprehensive information security programs. The FTC Safeguards Rule requires encryption of customer information, multi-factor authentication, and regular security testing—all of which must extend to AI systems processing financial data.

Building Your AI Governance Decision Framework

So how do you make practical decisions about which AI deployment model to use for specific business needs? Sentry recommends a portfolio approach:

Step 1: Classify Your Data and Use Cases

Create a simple classification system:

High Sensitivity:

• Customer financial information
• Healthcare records
• Proprietary algorithms or trade secrets
• M&A plans or strategic documents
• Personal identification information (PII) → Recommendation: Private AI or highly controlled tenant-isolated public AI with enterprise data protection agreements

Medium Sensitivity:

• Internal operational data
• Customer service interactions
• Marketing analytics
• HR information → Recommendation: Public AI with enterprise agreements and DLP controls, or private AI depending on volume and budget

Low Sensitivity:

• Public information summarization
• General market research
• Draft content creation for public use
• Coding assistance for non-proprietary applications → Recommendation: Public AI with basic usage policies and monitoring

Step 2: Assess Regulatory Constraints

Map each use case against your regulatory obligations:

• Does this involve HIPAA-protected health information?
• Are we processing payment card data subject to PCI DSS?
• Does this use case fall under EU AI Act high-risk categories?
• Are we subject to industry-specific compliance requirements (financial services, energy, etc.)?

Any "yes" answer requires enhanced controls, often pointing toward private deployment or specialized compliant public AI services.

Step 3: Evaluate Speed vs. Control Trade-offs

Consider:

• Time to value: How quickly do you need results?
• Scale: Will this be used by 10 people or 10,000?
• Customization needs: Do generic models work, or do you need domain-specific training?
• Cost sensitivity: What's your budget for infrastructure vs. per-use fees?
• Vendor lock-in tolerance: How comfortable are you with dependency on a specific provider?

Step 4: Implement Guardrails

Regardless of deployment choice, implement controls:

For Public AI:

• Data Loss Prevention (DLP) tools that scan prompts before sending
• Approved tool lists with clear usage guidelines
• Regular training on what data is safe to share
• Monitoring and alerting on sensitive data exposure
• Contractual protections in enterprise agreements

For Private AI:

• Access controls limiting who can use sensitive models
• Audit logging of all AI interactions
• Regular security assessments and penetration testing
• Model performance monitoring to detect drift or manipulation
• Clear procedures for model updates and changes

Measuring Success: Controls and Metrics That Matter

Effective AI governance requires measurement. Here are the key metrics Sentry tracks for clients:

Security and Compliance Metrics

• DLP Match Rate: Percentage of AI prompts flagged for sensitive data (target: declining over time as training improves)
• Incident Count: Number of data exposure incidents related to AI use (target: zero)
• Policy Compliance: Percentage of AI usage through approved channels (target: >95%)
• Review Completion Rate: For high-risk prompts requiring human review, percentage actually reviewed before processing (target: 100%)

Adoption and Effectiveness Metrics

• Approved Tool Adoption: Percentage of employees actively using sanctioned AI tools (target: increasing)
• Shadow AI Detection: Number of unapproved tools detected (target: declining)
• Productivity Impact: Time savings or output increases from AI usage (target: measurable ROI)
• User Satisfaction: Employee feedback on AI tools and policies (target: positive sentiment)

Governance Metrics

• Risk Assessment Coverage: Percentage of AI use cases with completed risk assessments (target: 100%)
• Training Completion: Percentage of employees completing AI literacy training (target: 100% of AI users)
• Policy Update Frequency: Regular review and update of AI governance policies (target: quarterly reviews minimum)
• Board Reporting: Regular updates to leadership on AI risks and opportunities (target: monthly or quarterly)

Your Next Steps: Partner with Sentry for AI Governance

You're navigating a technology landscape that's evolving faster than ever. The decisions you make about AI governance today will determine whether AI becomes your competitive advantage or your biggest liability.

The challenge isn't choosing between innovation and security—it's implementing both simultaneously. That requires expertise in technology, an understanding of your business context, and experience helping companies through similar transitions.

At Sentry Technology Solutions, we've spent over 10 years helping businesses navigate complex technology decisions exactly like this one. We understand the challenges because we've worked with companies facing them every day. We know what it feels like to worry about data security while trying to stay competitive. We've seen the consequences of poor AI governance and the transformative impact of getting it right.

Here's how Sentry becomes your trusted guide through AI governance:

Assessment and Planning

We start by assessing your current AI usage—both approved and shadow AI. We identify your regulatory requirements, data sensitivity levels, and business objectives. Then we create a clear roadmap tailored to your specific needs.

Technology Implementation

Whether you need private AI infrastructure, secure public AI deployment, or a hybrid approach, we handle the technical complexity. We implement the right tools for your situation, integrate them with your existing systems, and ensure your data stays protected throughout.

Policy and Training

Technology alone isn't enough. We help you develop clear, practical AI governance policies that employees will actually follow. We provide training that gives your team the knowledge to use AI safely and effectively.

Ongoing Management

AI governance isn't a one-time project. We provide continuous monitoring, regular policy updates, and proactive management to keep you ahead of evolving risks and regulations.

With Sentry as your partner, you'll boost security, increase productivity, improve profitability, and gain the peace of mind that comes from knowing your AI initiatives are built on a foundation of sound governance.

Don't let another week go by with unmanaged AI risk in your organization.

The businesses thriving with AI aren't the ones with the biggest budgets or the most advanced models. They're the ones who built governance frameworks that let them innovate safely. They're the leaders who recognized that AI governance isn't just IT's responsibility—it's a strategic imperative that requires executive ownership and expert guidance.

Schedule your complimentary AI Governance Discovery Call today. During this consultation, we'll assess your current AI landscape, identify your highest-priority risks, and create a customized action plan for implementing effective governance without slowing down innovation.

Contact Sentry Technology Solutions to start your AI governance journey with a partner who understands both the promise and the risks of this transformative technology.

For more information on comprehensive AI strategy and implementation, visit our AI services page to discover how strategic technology partnerships can transform your business operations while keeping your data secure.

Sources:

¹ Magic Mirror Security, "The State of Enterprise AI in 2025: 17 Adoption, Risk, and Governance Insights," 2025. ² ISG, "State of Enterprise AI Adoption Report 2025," September 2025. ³ Menlo Ventures, "2025: The State of Generative AI in the Enterprise," 2025. ⁴ AI21, "Private AI vs. Public AI: What is the Difference?", November 2025. ⁵ Magic Mirror Security, "The State of Enterprise AI in 2025," 2025. ⁶ Magic Mirror Security, "The State of Enterprise AI in 2025," 2025. ⁷ Forrester Research, "79% of large enterprises implementing internal private clouds," 2023. ⁸ Software Improvement Group, "A comprehensive EU AI Act Summary," October 2025.