Shadow AI Is Exploding, Governance Needs to Catch Up
Generative AI (GenAI) has rapidly become a fixture within enterprises, often utilized without formal oversight. Sales teams leverage it for crafting emails, engineers employ it to generate and test code, and marketers depend on it for copywriting and ideation. This unregulated usage falls under the category of Shadow LLM or Shadow AI, which refers to the unsupervised use of GenAI tools that frequently evade detection.
Much like Shadow IT, visibility is vital for security teams to understand the GenAI tools in operation, their usage patterns, and the users who pose the most significant risks. This trend is not driven by malice; users are drawn to GenAI tools due to their accessibility and productivity. Nevertheless, the ease of deployment and the lack of monitoring create a landscape where potential risks can flourish.
The statistics underscore this concern. A recent report by Palo Alto Networks indicated that GenAI traffic has surged by 890%. Furthermore, a survey of European legal teams revealed that while over 90% of firms are utilizing AI tools, merely 18% have implemented any formal governance structures.
As technology outpaces governance, organizations face risks such as exposing sensitive data, automating decisions without oversight, and creating blind spots in GenAI usage. To mitigate these risks, it is imperative for companies to establish a comprehensive GenAI policy that ensures safety in terms of regulation, compliance, and security.
What an Effective GenAI Policy Should Enforce
A well-structured GenAI policy should not hinder productivity; instead, it should ensure the reliability of the tools employees depend on, especially when these tools begin making decisions or processing data on behalf of the business. The policy should encompass six crucial areas:
1. Approval of GenAI Chatbots and Third-Party Applications
No GenAI tool should be approved without a thorough review. This includes assessing the tool’s functionality, its integration with existing systems, its developer, and its data handling practices.
2. GenAI Application Inventory and Ownership Assignment
To secure what is in use, every GenAI application—whether internal or external—must be documented in a centralized inventory. Clear ownership must be assigned to ensure accountability.
3. Access Controls and Permissions Management
GenAI tools should adhere to the same access protocols as other applications. This entails limiting visibility and actions based on user roles while regularly reviewing these permissions.
4. Logging and Audit Trails
In the event of an issue, understanding the sequence of events is crucial. Logging all interactions with GenAI across data flows for both inputs and outputs, along with alerting administrators to risky behaviors, is essential.
5. Testing and Red Teaming
Assuming GenAI systems will function as intended can be perilous. These systems should undergo rigorous testing prior to deployment and continuously thereafter. This process includes red teaming, simulations, and evaluations for vulnerabilities like prompt injection and compliance with data protection regulations.
6. Enforcement of GenAI Usage Guardrails
Policies are ineffective without enforcement. Guardrails that outline permissible tools, data access limitations, and requirements for human intervention should be integrated within the system.
Building the Right GenAI Policy from Day One
Crafting a policy is one challenge; implementing it effectively is another. Many organizations have established GenAI guidelines, but fewer have developed the governance structures necessary for consistent application across teams and tools. Often, these policies exist in documents that are rarely consulted or appear straightforward in theory but fail in practical application.
The disconnect between policy and actual operations must be addressed. This includes establishing the appropriate controls, ensuring visibility, and assigning responsibility for policy upkeep. Once GenAI becomes embedded within business operations, the policy transforms from a mere document into a critical safety net. If this safety net is absent when a problem arises, it may already be too late.
