Navigating Enterprise Data Risks in the era of Generative AI: Top 10 Concerns for Innovators, Security, and Governance Leaders

Dec 4, 2023

As we embark on this journey into Generative Artificial Intelligence (Gen AI), the horizon is both promising and challenging. This era is characterized by the rise of Large Language Models (LLMs), presenting innovators, security experts, and governance leaders with increasingly critical considerations. In this article, we’ll explore the top 10 issues that demand our attention in the realm of Gen AI, with a specific focus on enterprise data— the crown jewels of LLM-powered monetization.

In the landscape of securing AI, several frameworks have emerged, including OWASP Top 10 for LLMs, MITRE ATLAS, Google Secure AI Framework, and NIST AI RMF. The ongoing discourse around attacks, threats, and vulnerabilities in the emerging LLM stack is already vibrant.

However, our top 10 framework offers a unique perspective. Unlike others, our primary lens is sharply focused on data and access to enterprise systems.

1. Comprehensive AI Bill of Materials (BOM) with Integrated Change Management:

Maintaining a meticulous inventory and BOM of all deployed, approved, and authorized AI Models, LLM apps, pipelines, and related AI technologies. The integration of change management and operational control ensures a dynamic approach to AI governance.

2. Data Access Visibility and Control:

Ensuring transparency by pinpointing which LLM apps, Retrieval Augmented Systems, pipelines, vector databases, and models have access to enterprise data. Establishing robust access control mechanisms to govern and monitor data access.

3. Authorization and Governance Framework:

Developing a resilient authorization and governance framework tailored for LLM apps, models, algorithms, and data. This framework becomes the backbone for maintaining control in the era of Gen AI.

4. Model Access Policies:

Defining guardrails, policies, and approval processes that dictate how models access and learn from enterprise data. This ensures responsible and ethical use of AI models.

5. Granular Access Control for Multimodal Data:

Addressing the evolving landscape of data access control by implementing granular authorization for multimodal sensitive data. This is crucial in adapting to the diverse needs of LLM apps.

6. Data Classification for Unstructured Information:

Establishing a robust data classification schema to effectively handle unstructured content. This accommodates the unique requirements posed by LLM apps operating in unstructured data environments.

7. Identity Verification and Responsible AI Use:

Verifying and ensuring that only authorized personnel and identities leverage Gen AI responsibly within their scope of responsibility and accountability. Detecting malicious intent and preventing unauthorized access and use of AI models.

8. Content Appropriateness and Authorization:

Implementing measures to verify and ensure that generated content is appropriate and authorized for verified users. This is crucial in maintaining ethical AI practices.

9. Preventing PI and Sensitive Information Disclosure:

Implementing safeguards to prevent the leakage of Personally Identifiable (PI) and confidential information across the LLM stack. Proactive measures are essential in safeguarding sensitive data.

10. Ensuring Compliance and Governance on Data Sharing:

Ensuring that data sharing practices with LLMs, models, and LLM stacks, including third parties, adhere to data privacy, residency, sovereignty, and regulatory requirements. This is vital for compliance with AI regulations governing the movement of enterprise data.

In conclusion, as we embrace the potential of Gen AI, it is imperative to address these concerns head-on. By implementing robust frameworks, policies, and safeguards, we can navigate the challenges and usher in a new era of responsible and secure AI innovation. I would appreciate community feedback to focus on enterprise data risks in the era of Gen AI. My thanks to Al Ghous and Kenneth Foster for your inspiration, encouragement, and review.

We are inviting community feedback on this Generative Top 10 concerns for enterprise data. Share your comments here.