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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.
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 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 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.
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.
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.
Let's break down the practical differences that matter to your business:
| Consideration | Public AI Models | Private AI Models |
|---|---|---|
| Data Control | Data processed on provider's infrastructure; limited visibility into usage | Complete data sovereignty; never leaves your environment |
| Security | Shared infrastructure; potential for cross-tenant exposure | Dedicated infrastructure; isolated from external threats |
| Compliance | Provider-managed; may not meet industry-specific requirements | Built to your specifications; meets HIPAA, PCI, FTC Safeguards |
| Cost Structure | Pay-per-use or month; can escalate quickly at scale | Higher upfront investment; predictable ongoing costs |
| Customization | Limited; models designed for general use | Extensive; tuned to your data and workflows |
| Speed to Deploy | Immediate; start using today | Weeks to months; requires infrastructure setup |
| Performance | Generic; may require more prompting | Optimized; understands your business context |
| Vendor Lock-In | High; dependent on provider's roadmap and pricing | Lower; you control the stack |
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.
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:
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:
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:
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:
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:
AI governance isn't just good practice—increasingly, it's the law. Regulations vary by industry and geography, but several key frameworks demand attention:
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:
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:
Many healthcare providers opt for private AI deployment to avoid the complexity of ensuring HIPAA compliance with third-party AI services.
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.
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.
So how do you make practical decisions about which AI deployment model to use for specific business needs? Sentry recommends a portfolio approach:
Create a simple classification system:
High Sensitivity:
Medium Sensitivity:
Low Sensitivity:
Map each use case against your regulatory obligations:
Any "yes" answer requires enhanced controls, often pointing toward private deployment or specialized compliant public AI services.
Consider:
Regardless of deployment choice, implement controls:
For Public AI:
For Private AI:
Effective AI governance requires measurement. Here are the key metrics Sentry tracks for clients:
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:
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.
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.
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.
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.