All security professionals will remember the challenges of securing the bring your own device (BYOD) era.
Now, there’s a new security threat to contend with: BYO-AI. That’s “bring your own AI”–and it’s causing data breach risks everywhere.
Here’s what you need to know.
The security risks of BYO-AI
Whether your organization permits generative AI applications or not, your employees are likely using them in some capacity. Who can blame them? As McKinsey research shows, generative AI tools can supercharge productivity, creativity, and efficiency.
However, using generative AI tools is fraught with risks. Let’s take a deeper look at what’s at stake.
Data leaks
Generative AI tools, like ChatGPT, work by analyzing user inputs, which can include text, images, and audio. When these queries contain public information, the risk is usually low.
However, when sensitive data such as personally identifiable information (PII) or confidential source code is involved, there’s a higher risk of data leakage.
This risk stems from how these tools operate. To become more accurate and useful, AI models like ChatGPT need to learn from the data they process.
Once confidential information is entered, it becomes part of the model’s learning framework, making it challenging to maintain strict security controls. The AI’s improvement relies on the data it receives, which can complicate efforts to safeguard sensitive information.
Compliance complexity
Inputting sensitive information into generative AI models introduces substantial compliance challenges. This can place organizations at odds with regulations such as GDPR, CCPA, and others.
The primary issue stems from the opaque nature of generative AI applications. Once data is shared with these tools, it becomes nearly impossible for organizations to trace its path or determine with whom it has been shared.
This makes critical tasks like data minimization, anonymization, and erasure nearly impossible to perform, complicating efforts to adhere to regulatory requirements.
Credentials compromise
The accessibility of third-party generative AI platforms raises significant concerns about data theft. Many employees tend to reuse passwords across multiple accounts, making it easier for threat actors to hijack employee generative AI accounts.
This kind of insider threat risk is heightened if robust monitoring capabilities are not in place to detect and prevent account hijacking.
Supplier contracts
Supplier contracts have become stricter lately, requiring certifications and proof of strong security measures before partnerships begin.
However, tools like ChatGPT can complicate supply chain risk management for both suppliers and customers. Should an employee share sensitive client data with these AI tools, it could violate the contract and lead to legal issues.
How to secure BYO-AI
Some organizations have adopted a relaxed stance to generative AI, allowing employees to use these tools without established governance policies, often due to uncertainty about creating effective guidelines.
However, this approach will only lead to grave consequences later down the line.
Thankfully, the remedy is quite simple: transfer existing governance frameworks to secure BYO-AI.
Essentially, treating sensitive data within generative AI tools parallels handling it within cloud applications or corporate networks. Here’s how it breaks down:
- Identify, categorize, and monitor sensitive data within generative AI applications.
- Establish precise access controls for sensitive data in generative AI platforms, tailored to users’ roles and permissions.
- Reinforce data security measures with clear acceptable usage policies and training.
- Monitor user activities to detect any signs of data misuse or unauthorized sharing.
Securing AI with AI
Many organizations are unaware that security vendors are now tapping into the power of generative AI to develop specialized tools that secure BYO-AI.
Take, for example, next-gen data loss prevention (DLP) tools tailored for platforms like Bard and ChatGPT—they’re designed to significantly reduce the risk of data exposure in generative AI tools.
Here’s a closer look at how they work:
- Easy bi-directional data discovery and redaction: Next-gen tools quickly scan your generative AI apps to spot sensitive data in both prompts and responses. When they discover it, they can redact or block user actions according to your usage policies.
- Better audit efficiency: You can seamlessly conduct searches and access relevant generative AI interactions for e-discovery requests, audits, or compliance reviews.
- Active learning: Promote heightened security awareness among employees with active learning. In case of a compliance or security policy breach, next-gen DLP tools automatically issue training nudges that prevent data leakage whilst educating users.
- Internal visibility: Gain comprehensive insights into employee actions through robust logging and auditing features. This enables early detection of repeat offenders, compromised accounts, and insider threats, preempting potential data breaches.
Plus, many of these tools are easy to deploy—some even no-code or low-code—so you can secure BYOAI in just minutes.
Secure BYO-AI today
Discover how Polymer DLP can help you gain control over generative AI usage. Book a demo today.
