5 AI security platforms with compliance features compared

Enterprise AI has moved into native desktop apps, IDE plugins, and autonomous agents calling APIs and MCP servers, and many of those interactions never touch a browser.

The file-, URL-, and packet-centric tools most security teams already own weren’t built for conversational AI or agents that take action, which leaves gaps right where AI is moving from pilot to production.

Leading AI security platforms with compliance features may provide capabilities such as prompt and response inspection, shadow AI discovery, agent and MCP governance, and compliance evidence generation, although capabilities vary significantly by vendor. This article compares five of them, WitnessAI included, on deployment model, audit and traceability, agentic and MCP coverage, regulatory framework alignment, and enforcement depth beyond binary allow-or-block.

Key takeaways

• Compliance-ready AI security requires more than app discovery; teams need prompt and response inspection, policy enforcement, audit trails, and mappings to frameworks such as the EU AI Act, NIST AI RMF, and ISO/IEC 42001.
• The five vendors differ in architectural focus: WitnessAI provides unified governance across employees, AI models, applications, and agents; Cato embeds AI controls into SASE; Check Point with Lakera emphasizes runtime defense; Zenity centers on agents; and HiddenLayer focuses on model lifecycle security.
• Agentic AI and MCP coverage matter when agents can call APIs or tools, because compliance evidence depends on inventory, least-privilege controls, identity attribution, and exportable audit trails of actions.
• The best fit depends on deployment model and proof needs: regulated enterprises with broad AI usage should prioritize coverage across employees, models, applications, and agents, while narrower use cases may fit stack-integrated or specialized tools.

What are AI security platforms with compliance features?

AI security platforms with compliance features secure AI interactions across employees, models, applications, and agents while producing much of the evidence regulators and auditors require. They combine AI-specific threat detection with governance controls: shadow AI discovery, policy enforcement, audit trails, and mappings to frameworks like the EU AI Act, NIST AI RMF, and ISO/IEC 42001.

These platforms exist because file-, URL-, and packet-centric controls address a different problem. A CASB can discover which AI SaaS applications employees access and enforce data policies on them. What it cannot do is read the prompt, classify the intent behind it, or see a local model and an agent calling an API from a build server. 

Traditional DLP monitors file transfers and network traffic, yet generative AI tools often operate within chat interfaces, where users copy and paste text that these approaches may not inspect.

The compliance dimension raises the bar further. Prompt injection can alter model behavior and expose sensitive information. The EU AI Act requires human oversight and record-keeping for high-risk systems, and other AI management standards call for accountability, transparency, and data privacy.

Meeting these needs requires inspecting prompts and responses, discovering shadow AI, generating exportable audit trails, and mapping controls to named frameworks.

FOR COMPLIANCE

What Does AI Compliance Look Like?

WitnessAI automatically logs every AI interaction, masks sensitive data in real time, and enforces regulatory policies across every region and business line. Audit-ready from day one.

See WitnessAI For Compliance →

AI security platforms with compliance features compared

Each of the five platforms below approaches AI security and compliance from a different starting point, and the fit depends on where AI governance sits in your stack. Some extend SASE. Others build on application defense or MLOps workflows, while unified AI interaction governance spans human employees and AI agents.

WitnessAI

WitnessAI is built for enterprises that need unified AI security and governance across employees, models, applications, and agents. It governs the human and digital workforce from a single console, combining network-level inline deployment for employee governance with API-based runtime protection for models, applications, and agents.

The platform is organized into three core capabilities: 

• Observe discovers shadow AI and agentic infrastructure across native apps, IDEs, local runtimes, and MCP deployments across all deployment surfaces.
• Control uses intent-based policies to classify AI interactions and apply governance actions based on business context.
• Protect delivers bidirectional runtime defense for models, applications, and agents across prompts and responses.

Additionally, WitnessAI Attack delivers automated adversarial testing before deployment. It uses multimodal attacks, multi-step jailbreaks, comprehensive fuzzing, and reinforcement learning attacks to find vulnerabilities in models and agents while they can still be fixed.

For example, organizations can route sensitive AI interactions to approved internal models based on policy. A data tokenization approach protects sensitive information before it reaches a third-party model, then rehydrates the response so the user still receives a usable output.

Pros

• Discovery reaches Claude Desktop, VSCode with AI extensions, ChatGPT with plugins turned on, and local agent environments including LangChain, LlamaIndex, CrewAI, AutoGPT, and custom implementations.
• Deployment does not require endpoint agents or browser extensions, and coverage extends to native desktop applications, including Windows Copilot and Microsoft 365.
• Identity-linked audit trails trace agent actions back to a human identity, and MCP server connections and tool calls can be governed through organization-wide policy enforcement.
• Advanced sensitive-data detection using NER-D (Named Entity Recognition, Double-pass), which classifies data by meaning rather than format, enabling immediate enforcement for proprietary concepts (e.g., deal terms, trading strategies) without labeled datasets

Cons

• Pricing requires vendor engagement rather than self-service evaluation.

Pricing

WitnessAI uses custom enterprise pricing with no published tiers. Deployment options include Proxy Integration through existing SSE infrastructure, Witness Anywhere for agentless visibility without proxy integrations, API Integration for runtime model and agent protection, and MCP Integration for protocol-level agentic security. Buyers need to contact WitnessAI for a scoped quote.

Who is WitnessAI best for?

WitnessAI fits Global 2000 enterprises in regulated sectors, including financial services, healthcare, airlines, and telecoms. These organizations often need to govern both employee AI use and autonomous agents from a single console. It suits teams that want intent-based enforcement and identity-linked audit trails without endpoint rollouts.

Cato Networks

Cato Networks fits buyers evaluating AI controls inside a SASE architecture. The tool discovers shadow AI, uses context-aware inspection for prompts, and enforces real-time controls on employee use of AI tools and agents.

It ships pre-defined DLP rules that monitor uploads for PII, financial data, access keys, and legal data by default. The integrated AI Firewall extends to internal AI applications and agents across on-premises and cloud environments, tying enforcement, access control, and audit evidence into the existing SASE operating model.

Pros

• AI Security Posture Management capabilities enable continuous discovery and remediation of AI security and compliance risks prior to production.
• Security teams gain evidence and policy mappings suitable for legal, risk, and audit stakeholders.
• The platform supports enforcement of acceptable-use and compliance policies, as well as real-time detection of unauthorized data exchange with public AI services.

Cons

• Cato AI Security is licensable on its own, but enforcement still runs through Cato’s network. Organizations that steer traffic through a different SASE or SSE vendor should confirm what coverage looks like without Cato in the data path.
• During evaluation, validate reporting depth, UI workflows, and navigation, especially because the broader Cato platform existed before the AI Security module.
• Enforcement relies on the Cato SASE path, which creates visibility blind spots for native applications and traffic running outside the network tunnel.
• Purpose-built for network security extension rather than intent-based workforce governance, limiting depth for context-aware policy routing.

Pricing

Cato doesn’t publish specific pricing for Cato AI Security on its site. The AI capabilities are bundled into the broader SASE subscription as part of custom pricing. Buyers need to contact Cato for a quote tied to their SASE deployment.

Who is Cato Networks best for?

Cato Networks is a good fit for organizations already standardized on, or planning to adopt, the Cato SASE Cloud Platform. It suits teams that want AI governance folded into a broader network security subscription. Organizations evaluating standalone AI security may prefer to compare Cato against dedicated AI platforms.

OBSERVE

Your Employees Use 5x More AI Tools Than You Think

WitnessAI scans your entire network to catalog every AI app, agent, and conversation. No endpoint clients or browser extensions are required.

See How Observe Works →

Check Point (with Lakera)

Check Point Software is an enterprise cybersecurity vendor whose AI capabilities center on runtime defense for AI applications and agents. The platform concentrates on runtime guardrails and adversarial testing at the model layer.

Lakera Guard applies runtime guardrails to LLM inputs, outputs, and data flowing through RAG and MCP servers, enforcing protections against prompt attacks, data leakage, and content violations.

Lakera Red, powered by the Gandalf platform, provides continuous red teaming, and AI gateway controls handle application-layer enforcement. Check Point’s broader AI Defense Plane connects these capabilities to its existing security portfolio.

Pros

• Guardrails offer low-latency, multilingual screening with sensitivity tuning.
• AI gateway capabilities include unified audit trails, organization-level settings, team-level overrides, and project-level guardrails.

Cons

• Coverage of employee AI usage across endpoints and native desktop apps leaves visibility gaps on human-AI interactions.
• Enforcement emphasizes guardrail blocking more than intent-based policy routing of enterprise workflows.

Pricing

Check Point doesn’t publish specific pricing for Lakera Guard or the AI Defense Plane on its site. Named tiers exist for Unified Security Management, but no dollar amounts are listed. You can book a demo or sign up for a free Lakera Guard account as an entry point, with enterprise pricing handled through custom quotes.

Who is Check Point best for?

Check Point fits organizations building or deploying custom AI applications and agents. It suits teams that want prompt injection defense and continuous red teaming, particularly if they already run Check Point Infinity. If workforce-wide shadow AI discovery is your main requirement, compare it with platforms designed around network-level AI usage governance.

PROTECT

Runtime AI Threats Need Runtime Defense.

WitnessAI’s enterprise AI firewall delivers bidirectional runtime defense, blocking prompt injections, jailbreaks, and data exfiltration before they reach your models or your customers.

Explore Protect →

Zenity

Zenity is an agentic AI security platform that spans SaaS, cloud, and endpoint environments. It is purpose-built for AI agents running across SaaS, home-grown agentic platforms, and end-user devices, with particular focus on copilots and agents inside Microsoft, Salesforce, and ServiceNow.

Zenity inventories agents and configurations, tracks tool invocations, and governs MCP servers, plugins, and skills. It detects prompt injection attempts, credential exposure, and unauthorized agent actions, and it supports contextual incident response through modules that prevent risky actions and surface threat signals.

The centering on the agentic layer means less emphasis on general employee AI prompt governance, which matters when compliance teams define what audit evidence they need.

Pros

• Detailed audit trails of agent activity support compliance, investigations, and risk management for agentic environments.
• Governance of MCP servers, plugins, and skills addresses the new attack surface OWASP identifies around tool poisoning and prompt injection in MCP deployments.
• The product focus reflects depth on agentic threat patterns and governance gaps.

Cons

• Network-level employee prompt governance across native desktop applications and general shadow AI usage is more limited.
• Compliance positioning is less explicit about article-by-article or function-level control mappings for AI regulatory frameworks.
• The agent-focused scope may require pairing with another tool for full-workforce human and agent coverage from one console.

Pricing

Zenity doesn’t publish pricing on its site and operates through a demo-and-direct-sales motion. There are no listed tiers or dollar amounts. Buyers need to contact Zenity directly to scope a deployment.

Who is Zenity best for?

Zenity fits organizations whose primary concern is securing and governing AI agents and copilots across SaaS platforms, home-grown agentic systems, and endpoints. It suits teams deep into copilot deployments on Microsoft, Salesforce, or ServiceNow.

HiddenLayer

HiddenLayer is an independent AI security vendor focused on ML model security across the AI lifecycle. It addresses predictive, generative, and agentic AI, with a platform anchored on the model lifecycle rather than employee interactions.

HiddenLayer secures AI systems across development and deployment through four capabilities: AI Discovery for automated asset detection, AI Supply Chain Security, AI Attack Simulation, and AI Runtime Security.

It integrates with common AI development platforms for pre-deployment model scanning and for ongoing monitoring of behavior after release. The platform is most relevant when AI security is owned by AI engineering, data science, or MLOps teams, and its compliance value comes from lifecycle controls around models and AI assets.

Pros

• Model validation and policy enforcement run before deployment, with monitoring continuing after release.
• Supply chain security and pre-deployment scanning address risks before models reach production, integrated into ML platforms.
• HiddenLayer positions its AI detection and response toward EU AI Act alignment, helping enterprises support compliance for agentic systems.

Cons

• Network-level discovery and governance of employee shadow AI usage are more limited in breadth
• The MLOps-oriented scope serves AI development teams more than workforce-wide AI usage governance.

Pricing

HiddenLayer doesn’t publish pricing tiers or dollar amounts on its official site. Buyers need to contact HiddenLayer directly for an accurate quote.

Who is HiddenLayer best for?

HiddenLayer fits organizations building and deploying their own ML models. It suits teams that need supply chain security and pre-deployment scanning, with runtime defense integrated into MLOps workflows. If your main need is governing employee and agent AI usage, you may need capabilities outside the model lifecycle.

PLATFORM OVERVIEW

Stop Choosing Between AI Innovation and Security

WitnessAI lets you observe, protect, and control your entire AI ecosystem without slowing down the business. Enterprise AI adoption, without the risk.

See How It Works →

Choosing the right AI security platform with compliance features

The right AI security platform with compliance features depends on how far AI has spread across your organization and what evidence your auditors need.

As AI moves from a productivity tool to operational infrastructure, the architectural choice shapes what you can see, govern, and prove. When evaluating options, consider the following factors:

• Coverage across the AI surface: Assess whether the platform spans employees, models, applications, and agents, or whether it addresses only one layer such as SASE, endpoints, or MLOps.
• Deployment model: Decide between network-level or agentless deployment, endpoint agents, or API-based integrations based on how your AI traffic actually flows.
• Enforcement depth: Look beyond binary allow-or-block controls to platforms that support more granular governance actions based on policy and business context.
• Agentic and MCP support: Confirm the platform can inventory MCP servers, govern tool calls, and enforce least-privilege access as autonomous agents take on more actions.
• Audit and compliance evidence: Prioritize identity-linked audit trails and exportable mappings to frameworks like the EU AI Act, NIST AI RMF, and ISO/IEC 42001.
• Data sovereignty and isolation: Check for single-tenant architecture, customer-controlled encryption, and self-host options where regulatory requirements demand them.
• Fit with existing stack: Weigh whether stack-integrated features are enough for narrow use cases or whether a dedicated platform is needed as AI adoption expands.

Govern AI adoption with confidence

Successful enterprise AI adoption requires governance across employees, AI models, applications, and agents while minimizing operational friction. A purpose-built AI security governance platform can connect workforce governance, agentic security, runtime defense, and compliance evidence in one operating model.

WitnessAI provides that bridge with network-level visibility across native applications, IDEs, and agentic integrations visible at the network layer. Intent-based classification lets teams allow, warn, block, route, and apply data tokenization or redaction where appropriate, so policies can go beyond block-only controls. Identity-linked audit trails connect agent actions back to human users, while single-tenant architecture with customer-controlled encryption supports data sovereignty requirements. The result is a control model that supports experimentation, production deployment, and audit readiness at the same time.

Ready to see it in action? Book a demo to explore how WitnessAI governs your human and digital workforce from a single platform.