Top 6 AI Security Solutions for Protecting Sensitive Data

IBM's 2026 Cost of a Data Breach Report found that 43% of organizations with a breach involving AI had shadow AI as a factor, more than double the 20% figure from the year before. That number alone explains why "AI security solutions" has become one of the most searched categories in enterprise security, and also why the category is now crowded with three very different kinds of products all claiming the name.

Closing the gap between what AI tools can reach and what security teams can actually see requires purpose-built controls. But before you evaluate any vendor, it helps to know which problem you're actually solving. This guide breaks down the top AI security solutions built specifically to protect enterprise data, explains how they differ from the adjacent categories crowding the same search results, and helps you evaluate which platform fits your environment.

This guide is published by Forcepoint. We evaluate our own product against the same criteria as every vendor listed. This post was last reviewed in October 2026.

The Three Different Problems Called "AI Security"

Searching for AI security solutions surfaces three distinct categories, and conflating them is the fastest way to buy the wrong tool.

AI-powered security operations tools use AI to do security work faster, threat detection, SOC automation, behavioral analytics. They don't protect AI systems, they use AI to protect everything else.

AI and model security tools protect the AI system itself: model scanning, adversarial testing, prompt injection defense, agent red-teaming. This is a real and growing category, and it isn't what this guide covers. If runtime enforcement and model-layer defense are what you need, Forcepoint's partnership with F5 addresses that layer specifically.

AI data security tools protect the sensitive data that moves through AI systems, what sanctioned tools like Copilot and ChatGPT Enterprise can see, what shadow AI tools are quietly accessing, and what autonomous agents touch when they act on a user's behalf. This is the category this guide covers, and it's the one most buyers searching this term are actually trying to solve.

How We Evaluated These AI Security Solutions

To build this list, we researched every vendor the same way. For each one, we reviewed:

• Public product documentation, including datasheets, technical docs, pricing pages and release notes
• Independent analyst evaluations, including Forrester Wave and Gartner Magic Quadrant placements where available
• Each vendor's publicly documented integrations, deployment model and platform coverage
• Verified coverage across the three AI risk surfaces: sanctioned AI, shadow AI and agentic AI

We scored every vendor, including Forcepoint, against the same criteria: data classification accuracy, coverage across all three AI risk surfaces, enforcement depth and integration with the security stack organizations already run. Placement on this list cannot be bought. No vendor paid to be included or ranked.

One scope note: this guide covers AI data security platforms specifically. Vendors built primarily for model scanning, red-teaming or runtime prompt defense aren't scored here, because they're solving a different problem for a different buyer. For a deep comparison of vendors built specifically for securing autonomous agents, see our guide to the top agentic AI security solutions. For a deep comparison of vendors built specifically for securing autonomous agents, see our guide to the top agentic AI security solutions. If you’re comparing data security platforms more broadly, not just the AI-specific layer, see how we rank the field overall.

What Are the Top-Rated AI Security Solutions?

The following six platforms represent the leading AI data security solutions for enterprises protecting sensitive data across sanctioned AI, shadow AI and AI agents. Each is evaluated on the same data-layer criteria, not on model security, network security or general cloud posture.

Forcepoint Data Security Cloud: Best Overall for Enterprise AI Data Security

Forcepoint Data Security Cloud is built around Self-Aware Data Security, an AI-native approach that identifies sensitive information the moment it's created and protects it continuously across every channel, including AI.

At its core is AI Mesh, Forcepoint's classification engine that discovers and classifies data across clouds, SaaS apps, endpoints and AI workflows under a single policy. That single policy is what separates Forcepoint from point solutions that handle one AI risk surface well and leave the other two uncovered.

In practice, that single policy answers several specific questions security teams ask most often:

• Who is actually using AI, and is it governed? Every sanctioned platform, shadow AI tool and AI agent lands in one inventory, tied to the user or agent behind it.
• How do we reduce risk in ChatGPT and Copilot without slowing people down? API connectors apply existing DLP policy with no change to how employees work, scaling from coaching to blocking.
• Can we audit every AI interaction? API connectors, endpoint shadow AI detection and a closed-loop audit trail cover which tools were used, by whom and what data moved.
• Can we investigate an AI-related incident with full data context? Forcepoint DDR ties file activity to the prompts, responses and uploads behind it, so a team can quarantine a conversation or revoke an overshared file directly.

Best for: Enterprises and government organizations that need unified visibility and adaptive enforcement across sanctioned AI, shadow AI and agents, without running three separate tools to cover three separate risks.

Cyera

Cyera is an agentless, AI-native data security platform built around rapid discovery and classification across cloud, SaaS and on-prem environments. It extends that discovery into AI specifically, detecting shadow AI tools and monitoring how data flows into sanctioned ones.

Key features: LLM-powered classification, agentless deployment, shadow AI and sanctioned AI detection, expanding DLP capability.

Best for: Security teams that want fast, broad data visibility across cloud and AI workloads without an agent-based rollout. For a closer look at how the two platforms compare directly, see Forcepoint vs. Cyera.

Microsoft Purview

Microsoft Purview offers deep native integration with Microsoft 365, Azure and Copilot, making it a natural fit for organizations already committed to the Microsoft ecosystem. Its classification and compliance tooling is mature within that environment.

Key features: Sensitivity labels, insider risk management, compliance manager, native Copilot data governance.

Best for: Organizations operating primarily inside Microsoft 365 that want to extend existing governance directly into Copilot. Coverage drops off meaningfully for any AI tool outside that ecosystem.

Varonis

Varonis built its reputation on automated data access remediation, and extended that into AI with Atlas, its AI security module that reached general availability in March 2026. Atlas adds AI asset inventory, AI-SPM and AI-specific detection and response on top of Varonis's existing DSPM and DDR foundation.

Key features: Atlas AI asset inventory, automated find-fix-alert remediation, data detection and response, insider risk analytics.

Best for: Organizations that want DSPM paired with the automated remediation Varonis is known for, now extended to AI agents and copilots. See Forcepoint vs. Varonis for a direct comparison.

BigID

BigID centers its platform on data discovery and classification, with AI governance layered on top rather than bolted on after the fact. Forrester named BigID a Leader in its Q2 2026 Wave for Sensitive Data Discovery and Classification, and BigID added Agentic Access Control in August 2026 to govern what AI agents can reach based on task scope and data sensitivity.

Key features: Forrester Wave Leader for discovery and classification, agentic access control, vendor AI risk assessment, privacy and compliance integration.

Best for: Organizations that want AI governance unified with an existing privacy and compliance program rather than run as a separate initiative.

Securiti (Veeam)

Securiti's DataControls Cloud provides unified data intelligence and controls spanning public cloud, SaaS and on-prem environments. Veeam acquired Securiti in October 2025, and the product continues to operate under its own brand while integrating into Veeam's broader data resilience portfolio.

Key features: Unified data controls across hybrid environments, AI-specific data discovery, now backed by Veeam's data resilience infrastructure.

Best for: Organizations already consolidating backup, recovery and data security under Veeam that want AI data governance in the same stack.

Where Each Platform Covers the Three AI Risk Surfaces

Overall ranking aside, the question that matters most is narrower: which of the three risk surfaces does each platform actually cover.

No platform other than Forcepoint currently shows strong, established coverage across all three surfaces at once. Several vendors, Varonis and BigID especially, are moving quickly to close the agentic gap, which is worth watching rather than treating as settled.

Agentic AI introduces risks most data security programs were not built to catch. We go deeper on each one in our breakdown of the seven agentic AI risks security teams need to address

Types of AI Data Security Solutions and When to Use Them

AI Data Loss Prevention (DLP) inspects prompts, uploads and AI-generated outputs for sensitive content and enforces inline block, redact or mask actions. Use it when your priority is stopping sensitive data from entering or leaving AI chat interfaces and SaaS tools. Our DLP for AI guide goes deeper on evaluating this specifically for generative AI environments.

Data Security Posture Management (DSPM) discovers, classifies and risk-scores data to limit AI exposure before it becomes a problem. The core use case: map shadow data and fix access controls before a copilot rollout surfaces data employees shouldn't see. Our DSPM guide covers this in more depth. The core use case: map shadow data and fix access controls before a copilot rollout surfaces data employees shouldn’t see. Our DSPM guide covers this in more depth. If you’re choosing between point solutions, we scored ten DSPM vendors head-to-head.

Shadow AI discovery finds the AI tools nobody approved, typically through web and endpoint monitoring rather than a dedicated gateway. Use it when your biggest unknown is which AI tools employees have already adopted on their own.

A different problem: securing the AI system itself. Model scanning, adversarial testing, prompt injection defense and agent red-teaming protect the AI system from attack rather than protecting your data from exposure through the AI system. It's a real and fast-growing category, and it sits outside what this guide evaluates. Forcepoint's partnership with F5 addresses that layer directly if runtime enforcement is what you need alongside data-layer controls.

How to Choose the Right AI Security Solution for Your Enterprise

The right platform depends on your environment, risk profile and maturity. Here's what to prioritize.

Start with data visibility, not enforcement. If you don't know where sensitive data lives across cloud, endpoints, SaaS and AI workflows, you can't build effective policy. Solutions with strong DSPM and classification should be your foundation.

Check coverage across all three risk surfaces. A platform that handles sanctioned AI well but can't see shadow AI, or covers both but has no agent-layer story yet, leaves a gap you'll have to fill with a second tool. Use the surface-by-surface table above rather than relying on a vendor's overall pitch.

Prioritize AI-aware classification accuracy. Regex-based classifiers generate significant noise in AI environments. Look for platforms that can distinguish context, such as a 16-digit number in a financial report versus a product ID, without burning out your team with false positives.

Confirm the agentic AI story is real, not roadmap. Several vendors on this list added agentic AI capability in the past six months. Ask for a live demonstration against an actual agent workflow, not a slide.

Evaluate ecosystem integration. The best solution connects to the identity, SIEM and endpoint infrastructure you already run, rather than asking you to replace it.

How to Implement AI Security Solutions Successfully

• Assess your current state first. Audit shadow AI usage, map sensitive data with DSPM and identify your highest-risk user groups, typically engineering, finance and sales, before configuring a single policy.
• Define risk-based policies, not just rules. Static allow or block rules break down quickly in dynamic AI environments. Build policies around data classes, user risk levels and behavioral context.
• Deploy in phases. Start with your highest-risk groups, integrate with IAM and endpoint tools, then expand to API connectors for Copilot and ChatGPT Enterprise.
• Extend coverage to agents deliberately. Don’t assume your sanctioned-AI and shadow-AI controls automatically extend to agentic workflows. Verify agent-layer coverage before you need it, and check it against the ten controls that close that gap.
• Govern cross-functionally. Bring security, legal, IT and compliance together for regular policy review. AI security decisions can't live in one silo.

Secure Data Across Your Whole Organization with Forcepoint Data Security Cloud

AI is changing how data moves and how fast risk can form. Tools built to cover one AI risk surface leave the other two exposed, and the gap between what organizations can see and what they can control keeps widening.

Forcepoint Data Security Cloud closes that gap under one policy, across sanctioned AI, shadow AI and agents, rather than asking you to stitch together point solutions for each. Whether you're governing Copilot, discovering shadow AI or getting ahead of agentic workflows before they scale, Forcepoint gives you the visibility and control to move forward with confidence.

Ready to see how it works? Explore Forcepoint AI Data Security or connect with our team to discuss your environment.