AI Security Buyer’s Guide: What to Look for in an AI Data Security Solution

Abstract

Enterprise AI teams are moving quickly from experimentation toward production, but the security requirements surrounding sensitive data are becoming harder to evaluate. Traditional controls can govern access to systems and applications, yet AI introduces new questions about how data is used across training, retrieval, model context, autonomous agents, and generated responses.

The AI Security Buyer’s Guide gives CISOs, data leaders, and AI/ML leaders a practical framework for defining what good AI data security should look like before they begin evaluating vendors. Rather than starting with a product demo, the guide helps teams establish requirements first and compare solutions against a consistent standard.

Across five requirement domains, the guide outlines the capabilities to require, the questions to ask vendors, the answers that should raise concerns, and a scorecard teams can use to evaluate competing solutions.

Key Themes

Protect Sensitive Data Before AI Uses It

AI data security needs to extend beyond perimeter and access controls. The guide emphasizes protection that stays bound to sensitive data as it moves through training, retrieval-augmented generation (RAG), prompts, inference, and model output—while preserving the format and relationships AI systems need to work effectively.

Govern Data and Agent Access with Consistent Policy

As AI agents gain more autonomy, organizations need controls that reach beyond login and application access. The guide outlines how centralized policy, contextual access decisions, and distributed enforcement can help govern what models and agents may retrieve, return, and act on across AI workflows.

Build Security into AI Workflows

Security controls that sit outside developer and data workflows can introduce friction or become difficult to scale. The guide shows what to evaluate when looking for protections that can operate inside existing AI and data environments, support production throughput, and minimize the need for major pipeline redesign.

Require Evidence, Not Just Logs

AI security also needs to produce verifiable evidence of how sensitive data was protected and governed. The guide highlights continuous monitoring, data lineage, policy evidence, audit support, and crypto-agility as important considerations when evaluating a solution for long-term enterprise use.

What You’ll Learn

• How to evaluate AI data security across five requirement domains: discovery, data-level protection, governance, operations, and compliance.
• Which capabilities should be considered must-have when protecting sensitive data used by models, agents, RAG systems, and AI applications.
• Why data protection should persist through training, retrieval, prompts, inference, and model output.
• What questions to include in an RFP to expose gaps in a vendor’s AI data security approach.
• Which warning signs can indicate that perimeter or access controls are being presented as complete AI data protection.
• How to evaluate whether security controls can operate inside developer workflows and support production AI at scale.
• What evidence, lineage, monitoring, and compliance capabilities to require from an enterprise AI data security platform.
• How to score vendors consistently using the guide’s five-domain vendor scorecard.