Top 6 AI Security Solutions to Protect Your Data

AI has fundamentally changed how data behaves, and that changes everything about how you protect it. Data is no longer created primarily by people inside known systems at predictable speeds. Today, AI models, copilots and automated agents generate, reshape and spread sensitive information across clouds, SaaS platforms and endpoints faster than most security teams can track. The result is a widening gap between what organizations can see and what they can actually control.

That gap is where breaches live. Closing it requires purpose-built AI security solutions that don't just monitor risk. They act on it in real time.

This blog post breaks down the top AI security solutions available today, with a particular focus on the top AI cloud security solutions built for enterprise-scale environments. It explains the different solution types and their use cases, and helps you evaluate which platform best fits your organization's needs. If you're earlier in your data security journey, our data security posture management guide is a strong starting point for building the right foundation.

What Are the Top-Rated AI Security Solutions?

The following platforms represent the leading AI security solutions for enterprises protecting sensitive data in AI-driven environments. When evaluating the top AI cloud security solutions specifically, scope matters: some platforms deliver deep cloud-native coverage, while others are stronger across hybrid environments spanning endpoints, email and on-premises systems. Each addresses some dimension of the problem — though their depth, breadth and integration vary significantly. 

#1. Forcepoint Data Security Cloud: Best Overall for Enterprise AI Security

Forcepoint Data Security Cloud is the only platform built around Self-Aware Data Security — an AI-native approach that knows sensitive information the moment it's created, adapts as risk changes and protects continuously across every channel where data flows.

At its core is AI Mesh, Forcepoint's proprietary classification engine that discovers and classifies structured and unstructured data across clouds, SaaS apps, endpoints, email, networks and AI workflows. This isn't a bolt-on AI layer — it's the foundation the entire platform is built on. Combined with behavioral analytics and adaptive risk scoring, Forcepoint enforces a single-policy framework that eliminates the fragmentation that plagues siloed tools.

Key capabilities include:

• Real-time discovery and classification via AI Mesh across hybrid environments
• Adaptive Data Loss Prevention (DLP) that responds to context, not just rules
• Data Security Posture Management (DSPM) to map and manage data exposure before AI workflows surface it
• Integrated user coaching so security becomes an enabler, not a blocker
• Unified coverage across endpoint, web, SaaS, email, network and AI

For security leaders who want to understand how DSPM specifically supports AI readiness, how DSPM secures AI is worth reading alongside this guide.

Best for: Enterprises and government organizations managing sensitive data across complex hybrid environments who need unified visibility, adaptive enforcement and compliance at scale.

#2. Microsoft Purview

Microsoft Purview offers deep native integration with Microsoft 365, Azure and Teams, making it a natural starting point for organizations already committed to the Microsoft ecosystem. Its data classification and compliance capabilities are mature within that environment.

Key features: Sensitivity labels, insider risk management, compliance manager, eDiscovery and audit.

Best for: Organizations primarily operating within Microsoft environments looking to extend governance and compliance controls.

Limitation to consider: Coverage degrades significantly outside the Microsoft stack, which is a meaningful gap for organizations running multi-cloud or diverse SaaS environments.

#3. Palo Alto Networks Prisma CASB

Prisma CASB provides strong visibility into cloud application usage, including shadow AI discovery. It integrates with Palo Alto's broader SASE platform, giving it an advantage for organizations already using Palo Alto for network security.

Key features: App discovery, inline and API-based inspection, data classification for cloud storage, shadow IT identification.

Best for: Organizations prioritizing cloud application governance and already invested in the Palo Alto ecosystem.

#4. Zscaler

Zscaler's zero trust architecture makes it a strong fit for securing web traffic and controlling access at the network layer. It can apply data inspection inline for traffic routed through its cloud proxy.

Key features: Zero trust network access (ZTNA), cloud-native web proxy, DLP for web traffic, AI app control.

Best for: Organizations focused on network-level zero trust and controlling employee access to web-based AI tools at scale.

#5. Cyera

Cyera is a cloud-native DSPM platform focused on discovering and classifying data across cloud environments. It provides strong visibility into where sensitive data lives, how it's exposed and what permissions are in place.

Key features: Automated cloud data discovery, risk prioritization, permissions analysis, compliance reporting.

Best for: Security teams that need to rapidly inventory cloud data exposure before rolling out AI tools or copilots.

#6. Wiz

Wiz specializes in cloud infrastructure security, with a particular strength in detecting misconfigurations, excessive permissions and vulnerability exposure across cloud environments. Its data security capabilities have expanded but remain infrastructure-oriented.

Key features: Cloud security posture management (CSPM), vulnerability management, secrets detection, infrastructure-as-code scanning.

Best for: Cloud engineering and security teams focused on infrastructure risk rather than data-in-motion or user behavior scenarios.

Types of AI Security Solutions and When to Use Them

Understanding the category landscape is just as important as evaluating individual vendors. AI security is a broad field, and different solution types address different parts of the problem.

AI Data Loss Prevention (DLP) inspects prompts, uploads and AI-generated outputs for sensitive content — PII, intellectual property, regulated data — and enforces inline block, redact or mask actions. Use it when your priority is preventing sensitive data from entering or leaving AI chat interfaces and SaaS tools. Learn more about how this applies in the security in the genAI era.

Data Security Posture Management (DSPM) discovers, classifies and risk-scores data to limit GenAI exposure before it becomes a problem. The key use case: map shadow data and fix access controls before a copilot rollout surfaces data employees shouldn't see. "Forcepoint DSPM is purpose-built for this.

Secure Web Gateway (SWG) / Cloud Access Security Broker (CASB) discovers shadow AI usage and enforces per-application policies across browsers and cloud environments. Use it when you need to control which AI tools employees can access and what data they can bring into those tools — at scale, across hundreds of apps.

Zero Trust Network Access (ZTNA) gates access to AI systems based on identity, device posture and behavioral risk signals. It prevents unauthorized uploads to internal or third-party AI systems, particularly for remote workers and contractors.

Runtime Protection monitors production AI models for anomalies, jailbreak attempts and unsafe outputs. Use it when you're deploying customer-facing AI applications or internal LLMs and need to ensure model integrity and output safety.

AI Security Gateways act as a proxy for LLM interactions, filtering inputs and outputs against prompt injection, data exfiltration and policy violations. The use case: secure direct API calls to models like OpenAI or Anthropic from internal applications.

How to Choose the Right AI Security Solutions for Your Enterprise

The right platform depends on your specific environment, risk profile and maturity. Among the top AI cloud security solutions, the differentiators aren't just features — they're architectural. Here's what to prioritize.

1) Start with data visibility, not enforcement. 

Many organizations jump to blocking before they understand what they're protecting. If you don't know where your sensitive data lives — across cloud storage, endpoints, SaaS apps and AI workflows — you can't build effective policy. Solutions with strong DSPM and classification capabilities should be your foundation.

2) Prioritize AI-aware accuracy. 

Traditional regex-based classifiers generate significant noise in AI environments. Look for platforms using AI-native classification engines (like AI Mesh) that can distinguish context — a 16-digit number in a financial report versus a product ID in a spreadsheet — without generating constant false positives that burn out your team.

3) Demand shadow AI coverage. 

Employees are using AI tools whether you've approved them or not. Your solution should give you visibility into hundreds of AI applications, not just the ones you've sanctioned. If you can't see it, you can't govern it.

4) Look for unified DLP and DSPM in a single framework. 

Platforms that separate discovery from enforcement create the exact visibility-to-control gap you're trying to close. A single-policy framework that connects classification to enforcement — consistently, across every channel — is the architectural advantage to look for.

5) Evaluate compliance template depth. 

If your organization operates across multiple regulatory frameworks — GDPR, PCI DSS, HIPAA, CMMC — the ability to apply prebuilt, auditable compliance policies dramatically reduces the operational burden on your team.

6) Assess ecosystem integration. 

The best solution is one that works with your existing identity, SIEM and endpoint infrastructure. Evaluate how easily the platform connects to your IAM systems, logging tools and ticketing workflows before you commit.

How to Implement AI Security Solutions Successfully

Deploying an AI security platform isn't a one-time event — it's a phased discipline. Here's how to approach it.

• Assess your current state first. Before configuring a single policy, audit your shadow AI usage, map your sensitive data with DSPM and identify the highest-risk user personas — typically engineering, finance and sales teams with broad data access. You need a clear picture of exposure before you can prioritize what to protect. The Forcepoint for AI Security use case page covers this framing in more detail.
• Define risk-based policies, not just rules. Static allow/block rules break down quickly in dynamic AI environments. Build policies around data classes, user risk levels and behavioral context — and use prebuilt compliance templates to accelerate the process for regulated data types.
• Deploy in phases. Start with your highest-risk groups using SWG and DLP, integrate with your IAM and endpoint tools, then expand to API connectors for tools like Microsoft Copilot and ChatGPT Enterprise. Don't try to protect everything at once.
• Monitor, tune and adapt continuously. Build dashboards that track AI-specific metrics: blocked actions, anomalous usage patterns, high-risk incidents. Review false positives weekly and refine classifiers accordingly. Data classification is never a one-time effort — it requires ongoing tuning as your data environment evolves.
• Govern cross-functionally. Form a cross-team AI council that brings together security, legal, IT and compliance stakeholders for regular policy reviews. AI security decisions can't live in a silo. The organizations that get this right treat it as a business enabler, not a security checkpoint.

Secure Data Across Your Whole Organization with Forcepoint Data Security Cloud

AI is changing how data moves, how risk forms and how fast both can escalate. Legacy tools built on static assumptions can't keep pace — and the gap between what organizations can see and what they can control keeps widening.

Forcepoint Data Security Cloud is built to close that gap. With AI Mesh at its core, it delivers unified discovery, classification and adaptive enforcement through a single-policy framework — so security teams can know where sensitive data lives, adapt as risk evolves and protect it everywhere without slowing the business down.

Whether you're managing shadow AI, rolling out copilots or hardening your posture before a compliance audit, Forcepoint gives you the visibility and control to move forward with confidence.

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