Curious about AI governance, but not sure whether it matters or even how to begin? This guide is for you. Read on to discover what AI governance is, why it’s important, and how to get started.
AI governance: What it is and why you need it
As Gartner recently pointed out, AI is no longer a luxury—it’s mission-critical for any enterprise aiming to thrive in the next decade. If you’re not scaling AI, you’re falling behind.
But scalability without governance is a disaster waiting to happen. Think of governance as your AI’s roadmap—a framework to ensure your tools are responsible, ethical, and reliable. Simply put, if you want to succeed with AI, you need solid governance.
If that’s not enough to persuade you, there’s also the opposite end of the spectrum together: what happens when governance isn’t good enough. And it can be catastrophic.
Last year, Google lost $100 billion in stock value in under 24 hours—thanks to a misstep by its AI bot, Bard. In an interview on CBS’ 60 Minutes, Bard confidently referenced a non-existent book, The Inflation Wars: A Modern History by Peter Temin, claiming it “provides a history of inflation in the United States.” This blunder wasn’t just embarrassing—it was a textbook case of an AI hallucination, where the model invents false information and presents it as fact.
The root cause? Poor AI governance. When AI systems are left unchecked, they produce unreliable outputs, degrade data quality, and erode user trust—creating long-term damage that far outweighs any short-term gains. Governance isn’t just about following rules; it’s about protecting your brand, your bottom line, and your reputation in a fast-evolving AI landscape.
The challenges of implementation
Right now, many companies are struggling with how to implement AI governance—or worse, ignoring it altogether.
For some, the knee-jerk reaction has been to ban generative AI entirely. But we strongly advise against this for two key reasons.
First, generative AI is a powerful tool that boosts productivity and creativity. Banning it stifles innovation. Second, banning AI often leads to shadow AI—employees using generative AI tools in secret. When AI is used without IT’s oversight, the risks of data breaches and compliance violations skyrocket.
Elsewhere, other companies are attempting to shape their AI governance using regulatory guidelines, like those from NIST or the EU. While these frameworks are helpful, they’re often complex and hard to implement. Worse, with the AI landscape evolving so quickly, today’s guidelines could be outdated or overhauled in just a few months. This means relying solely on regulations leaves companies constantly playing catch-up, rather than staying ahead.
The rewards to be gained
For all the complexity surrounding AI governance, becoming an early-mover is a must. Here’s why:
- Brand trust: Strong AI governance doesn’t just keep you compliant—it builds trust. Customers, investors, and regulators value companies that prioritize responsible AI. It’s not just a safeguard; it’s a market differentiator.
- Data security and compliance: A solid governance strategy goes hand in hand with data security, helping you to identify privacy and data breach risks before they escalate, and maintaining compliance with standards like the GDPR and CCPA.
- Competitive edge: A solid governance framework makes your AI processes smoother and faster. With clear protocols in place, you accelerate innovation and efficiency in tandem, freeing up resources to deliver results quicker than competitors still playing catch-up.
Getting started with AI governance
Beginning with AI governance can seem daunting and overwhelming. But it doesn’t have to be. The best route forward is simple: leverage existing governance frameworks to safeguard generative AI.
An effective generative AI governance program encompasses key principles just like any solid data governance policy: data quality, stewardship, protection, compliance, and management. Think of sensitive data in generative AI tools the same way you would in cloud applications or your corporate network. Here’s what that looks like:
- Discover, classify, and monitor: Implement bi-directional monitoring of sensitive data within generative AI applications to ensure visibility and control.
- Granular access controls: Set up access permissions based on user roles to limit who can interact with sensitive data in generative AI tools.
- Support security with policies: Back your data security controls with acceptable usage policies and comprehensive training to create a culture of awareness.
- Monitor user behavior: Keep an eye on user activities for signs of data misuse or improper sharing, ensuring accountability at all levels.
Harness AI for AI
The funny thing about AI governance is that you’ll need to use AI to achieve it. Yep, you read that write. Next-gen security providers like Polymer DLP are harnessing the power of machine learning and natural language processing (NLP) to deliver security tools specifically designed to enhance AI governance.
Here’s how our tool helps you achieve governance for AI:
- Data visibility: Our solution enhances your data visibility by offering an intricate, detailed perspective of unstructured data flows in SaaS applications. This is achieved by harnessing the power of AI, natural language processing and LLMs to significantly ramp up the speed and precision of classification across the cloud. The result is granular insight and control over data lineage, access and usage.
- Compliance: Lack of visibility and control over data—whether in transit or at rest—creates compliance headaches, making it difficult to maintain data residency, minimization, and protection. Enter Polymer: your self-governing compliance officer. We automate cloud compliance policies and protect sensitive data effortlessly. Our solution simplifies audits by automatically tracking, rectifying, and logging policy violations, ensuring compliance teams are alerted to high-risk incidents without needing manual intervention.
- Enhanced data security: CISOs recognize the critical risks that come with generative AI, including data exfiltration via conversational bots, intellectual property theft, and unauthorized customer data sharing. To tackle these issues head-on, it’s essential to establish robust guardrails before diving into AI deployment. That’s where Polymer for AI shines. With our solution, CISOs can seamlessly integrate LLM models like Cohere, ChatGPT, Anthropic, and custom APIs while ensuring a secure environment. Polymer DLP provides powerful features like bi-directional monitoring and remediation, scanning conversations to prevent data exposure.
- A culture that upholds governance: Polymer focuses on human risk management to foster a culture of compliance. Our platform delivers real-time nudges at the point of violation, empowering users to understand and correct their mistakes. This proactive approach has been proven to reduce repeat violations by over 40% in just days. Additionally, Polymer provides workflows that enable users to take responsibility for sharing sensitive data when it aligns with business needs, ensuring a balanced approach to data governance and compliance.
Ready to uplevel your AI governance strategy? Request a demo today.
