Generative AI tools are undoubtedly the future of workplace productivity. In fact, innovations like Bard and ChatGPT are already impacting employee workflows in a positive way.
As research from Deloitte shows, generative AI can be a growth catalyst for organizations of all sizes. From improving customer service to enhancing HR management, the power of generative AI knows no bounds.
Unfortunately, though, the security risks are similarly limitless. Indeed, generative AI introduces security issues all organizations are familiar with—namely, the risks of data leakage and data theft. However, how these risks unfold is new.
In response, some organizations have banned generative AI tools altogether, whilst others have plowed forward with adoption without considering the security implications.
Unsurprisingly, neither approach is wise. But there is a better way. Here, we’ll explore how organizations can implement effective data governance for generative AI and unlock the competitive efficiencies it offers, without the cybersecurity risks.
AI governance in the enterprise: the state of play
AI governance in the enterprise varies greatly, and it’s easy to understand why. The technology has rapidly spread through the working world in a matter of months, leaving regulators, compliance professionals, and security evangelists scrambling to implement sensible guidelines in its wake.
While the knee jerk reaction by many companies has been to ban generative AI, we caution against this for two main reasons. For one, generative AI is, overall, an excellent tool for improving productivity and creativity amongst employees. Banning it essentially hinders innovation.
Secondly, there’s the risk of shadow AI. Even when employers ban GenAI tools, their employees are still likely to use them, just more secretively. The problem with this, of course, is that generative AI used without the IT team’s knowledge is even more of a data security and compliance risk.
Other companies have attempted to shape their AI governance policies around regulatory guidelines from the likes of NIST and the EU. Whilst these guidelines are undoubtedly helpful, they can be complex to understand and complex to deploy. Moreover, with the AI landscape shifting so quickly, any guidelines could be updated, retracted and overhauled in a matter of months.
How to govern AI effectively
Some organizations have blown caution to the wind, allowing their employees to use generative AI without governance, because they’re unsure of how exactly to craft a valuable governance policy. Unfortunately, though, the fallout of this approach can be catastrophic.
Just look at Samsung, which suffered a generative AI data leakage after employees shared proprietary data with ChatGPT.
So, what’s the answer? It is surprisingly simple: lift existing governance frameworks and use them to safeguard generative AI.
As with all effective data governance policies, an effective generative AI governance program will encompass the following tenants: data quality, data stewardship, data protection and compliance, and data management.
In essence, this involves thinking about sensitive data within generative AI tools as you would sensitive data within cloud applications or the corporate network, which looks something like this:
- Discover, classify, and monitor sensitive data bi-directionally in generative AI applications
- Implement granular access controls to sensitive data in generative AI apps based on users’ roles and permissions
- Support data security controls with acceptable usage policies and training
- Monitor user behavior for evidence of data misuse or improper data sharing
The right tool for the job
Many organizations don’t realize that security vendors have begun to harness the power of generative AI to deliver security tools specifically designed to enhance AI governance.
For example, next-generation data loss prevention (DLP) tools built for generative AI platforms dramatically reduce the risk of data exposure in applications like Bard and ChatGPT, offering capabilities like:
- Streamlined bi-directional data discovery and redaction: Effortlessly scan your generative AI applications to swiftly identify sensitive data in both prompts and responses. Upon detection, apply redaction or blocking measures in accordance with contextual usage policies.
- Enhanced audit efficiency: Seamlessly execute searches and access pertinent generative AI interactions when confronted with e-discovery requests, audits, or compliance reviews.
- Active learning: Foster a culture of heightened security awareness among staff members through timely security prompts. In the event of a compliance or security policy breach, promptly deliver notifications pinpointing the violation, empowering employees with contextual insights for future adherence.
- Internal insight: Attain comprehensive visibility into employee actions via robust logging and auditing functionalities. This proactive approach enables the identification of recurrent offenders, compromised accounts, and malicious insiders, thus preempting potential data breaches.
In many instances, these tools have the added benefit of being no or low-code, making them rapid to deploy, so you can start building effective AI governance in just minutes.
To find out more about building a scalable AI governance program, read our free whitepaper on data governance and security in the age of generative AI.
