6 Best LLM Security Tools

Enterprise AI is delivering real results. Two-thirds (66%) of organizations report productivity and efficiency gains from AI adoption.

But the same systems driving those gains are creating new security challenges. As organizations embed AI deeper into critical operations, their exposure grows in ways that existing defenses weren’t designed to handle.

AI agents are at the center of that shift. Operating with elevated permissions across multiple systems, they represent one of the fastest-expanding attack surfaces in enterprise security today. Fragmented tools and legacy defenses were not designed to fully address autonomous, adaptive systems operating at machine speed.

This guide compares six LLM security platforms to help you evaluate the right security architecture for your enterprise AI environment.

Key Takeaways

• AI agents with elevated permissions across multiple systems represent the fastest-expanding attack surface in enterprise security, and legacy defenses weren’t built to protect them.
• Options range from browser-extension-based governance and SASE-integrated offerings to purpose-built platforms with network-level deployment, each with distinct trade-offs.
• More advanced platforms go beyond visibility to inspect both prompts and responses, enforce granular policies (route, redact, block), and extend protection into agentic workflows and native desktop apps.
• Evaluation should focus on deployment model, AI stack visibility, policy granularity, and whether the platform covers your full AI footprint.

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LLM Security Tools Compared

The platforms below span purpose-built AI security, browser-extension governance, and AI security integrated into broader infrastructure.

Witness AI

WitnessAI is a unified AI security and governance platform that positions itself as the confidence layer for enterprise AI. The platform’s architecture is defined by its network-level deployment, which captures AI activity at the network level without requiring endpoint clients, browser extensions, or SDK changes in supported deployment models. 

This approach extends visibility into native desktop applications, IDEs, and agentic environments, a defining architectural choice for teams seeking broad coverage with minimal rollout overhead. WitnessAI’s intent-based classification engine classifies the likely intent behind AI interactions rather than relying on keyword or regex matching.

The platform also extends its Observe, Control, and Protect framework to agentic environments, keeping human and digital workforce governance in a single console. As organizations adopt AI agents, security coverage scales with them, spanning agent and MCP server discovery, tool-call protection, and identity attribution.

Pros

• Bidirectional runtime defense inspects both prompts and responses, enabling data tokenization that can redact sensitive information before it reaches a third-party model, based on defined policies.
•  This addresses prompt-side and response-side risk in a single flow.
• Enforcement goes beyond binary allow-or-block decisions, supporting route and redact actions alongside intelligent policies configurable by role, intent, and context. That granularity lets teams tailor controls to specific business scenarios instead of enforcing a single policy across the organization.
• Agentic security capabilities include agent and MCP server discovery, agent behavior guardrails with tool-call protection, and identity attribution that enables attribution of agent actions back to associated human identities.

Cons

• As a focused AI security platform, it is not positioned as a bundled network infrastructure offering from a broader security vendor. Organizations seeking that type of consolidation may weigh this against the depth of AI-specific expertise.
• The company appears enterprise-focused. Smaller teams may find the scope beyond their immediate needs.

Who is WitnessAI best for?

WitnessAI is best for enterprises that need unified governance across their human and digital workforce, especially organizations that prioritize audit trails, data sovereignty, and more nuanced policy enforcement.

2. Harmonic Security

Harmonic Security is a browser-based AI governance and data protection platform built on an extension architecture that governs how employees interact with AI tools in the browser. It’s a straightforward approach for organizations whose AI usage is largely web-based.

Harmonic also offers an MCP Gateway designed for agentic workflows, extending the platform’s reach into emerging agent-driven use cases. The company positions its approach around using domain-specific context to inform policy decisions.

Pros

• The enforcement philosophy emphasizes targeted nudges instead of hard blocks. This can preserve employee velocity while still surfacing risk.
• Pre-trained small language models detect sensitive data in milliseconds without manual data labeling or complex rule creation.

Cons

• Browser-extension-based architecture may limit visibility into native desktop app activity, a limitation noted in industry analysis. AI activity in desktop IDEs or native Copilot integrations may sit outside that model.
• Dashboard and reporting customization may require additional evaluation. Organizations with complex reporting requirements should validate this area during a POC.

Who is Harmonic Security best for?

Harmonic fits organizations governing employee AI usage primarily through the browser, including healthcare organizations where HIPAA-related safeguards matter.

3. Lasso Security

Lasso Security is an AI security platform that brings together shadow AI discovery, runtime defense, AI red teaming, and agent governance into a single offering.

What sets Lasso apart architecturally is that it runs open-source models on its own GPUs, giving the company full control over its technology stack and enabling a GPU-based pricing model rather than the token-based pricing common elsewhere.

That same infrastructure underpins both the platform’s runtime performance and its cost structure. The company also operates a federal subsidiary for public sector buyers, positioning the platform across multiple market segments.

Pros

• An own-GPU inference architecture provides full control over the technology stack and avoids rate limits that can affect platforms that depend on third-party LLM APIs. That architectural independence may matter for buyers with performance or control requirements.
• The discovery scope covers a broad range of AI tools, which may help organizations with large SaaS footprints reduce shadow AI blind spots.

Cons

• Published latency figures vary across materials, so buyers should validate performance during evaluation.
• The GPU-based pricing model avoids per-token costs but may introduce cost-predictability challenges for organizations with variable AI workloads. The AWS Marketplace listing notes usage-based overage charges applied on top of contract pricing, which buyers should model against their expected volume before committing.

Who is Lasso Security best for?

Lasso fits organizations that are building or deploying LLM-powered applications and need both red teaming and runtime defense, particularly in the public sector.

4. Aurascape

Aurascape is an AI-native security platform focused on multimodal AI coverage across employee governance and AI development security.

Aurascape’s distinguishing capability is multi-format AI interaction decoding across text, code, images, video, and audio, a differentiator relative to text-focused tools.

The platform is organized into two pillars: “Safely Use AI” for employee governance and “Securely Build AI” for agentic development lifecycle security, framing Aurascape as covering both usage governance and build-stage security.

Pros

• Multi-format coverage across text, code, images, video, and audio fills a gap left by platforms focused mainly on text interactions. That makes Aurascape notable for organizations with broader exposure to multimodal technologies.
• Aurascape’s platform spans employee AI usage controls and development-oriented AI security capabilities through its dual-pillar architecture. Buyers looking for both governance and build security may find that combination appealing.

Cons

• Deployment requires an endpoint client that redirects AI-related traffic to the cloud for inline inspection rather than passive log collection.

Who is Aurascape best for?

Aurascape fits organizations where multimodal AI interactions and long-tail application coverage are priorities.

5. F5 AI Guardrails (formerly CalypsoAI)

F5 AI Guardrails centers on threat defense, DLP, governance, and content moderation for deployed AI models and agents.

To accelerate initial setup for regulated teams, the product ships with pre-built compliance presets for GDPR, HIPAA, and the EU AI Act. Its model-agnostic architecture includes preset configurations for popular enterprise and open-source AI models, making it relevant for organizations running heterogeneous model environments.

Pros

• The companion F5 AI Red Team product feeds adversarial testing findings directly into Guardrails policies, creating a closed-loop defense model.
• The platform focuses primarily on runtime security for deployed models and agents, with less emphasis on native shadow AI discovery or employee-level usage controls. F5’s own partnership with Forcepoint for data discovery and classification reinforces this scope boundary.

Cons

• Reviewers flag that “customization is limited in some areas,” and initial setup may require a solid understanding of AI and security systems.

Who is F5 AI Guardrails best for?

F5 AI Guardrails fits organizations already operating within the F5 ecosystem that need runtime defense with out-of-box compliance presets.

6. Cato AI Security (formerly AIM Security)

Cato AI Security is the AI security capability within Cato Networks’ SASE platform. The offering spans shadow AI governance, an AI Firewall, and AI security posture management.

Integrated with the broader Cato platform’s SASE-aligned architecture, the product focuses on visibility and control of AI use across the organization.

A modular adoption model lowers the barrier to entry, allowing organizations to purchase AI Security as a standalone solution before expanding to full SASE. For existing Cato customers, native integration may simplify stack alignment and reduce the need for a separate point solution.

Pros

• Cato positions its AI security as SASE-integrated, which may reduce the need for a separate point solution if you’re already evaluating or running Cato’s network platform.
• The modular adoption model lets organizations deploy AI security as a standalone capability or extend into SD-WAN, SSE, and ZTNA through the same platform. Plus, flexible deployment options support on-premises, cloud, or edge environments.

Cons

• Some users say it has limited advanced customization compared to traditional solutions, a steep learning curve, and high pricing.
• BYOD enforcement is also noted across multiple reviewer sources to be limited to API-based monitoring, without full enforcement capabilities.
• Even with modular purchasing, the offering is presented within Cato’s broader platform context. Teams looking only for a narrow AI-specific product may want to assess how that packaging fits their buying process.

Who is Cato AI Security best for?

Cato AI Security fits enterprises already on or evaluating Cato’s SASE platform that want to avoid adding a separate point solution.

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How to Choose the Right LLM Security Tools

Choosing the right LLM security tool starts with understanding where AI risk shows up in your environment. Follow these steps to narrow your shortlist and match the right platform to your operating model.

• Map your AI footprint. Inventory where and how AI is used across your organization. Identify whether usage is primarily browser-based or extends into native desktop apps, embedded copilots, or custom models. This determines the breadth of coverage you need.
• Define your deployment requirements. Evaluate how each platform deploys: as a browser extension, an endpoint client, a network-level proxy, or SASE-integrated. Match deployment models to your infrastructure constraints, rollout capacity, and IT overhead tolerance.
• Assess AI stack visibility. Determine what parts of the AI stack each tool can actually see. Can it inspect both prompts and responses? Does it cover agentic workflows, MCP servers, and tool calls? Gaps in visibility translate directly to gaps in protection.
• Evaluate policy granularity. Look beyond binary allow-or-block enforcement. Assess whether the platform supports nuanced actions such as route, redact, and context-aware policies that are configurable by role, intent, or business scenario. The more granular the controls, the less friction for end users.
• Confirm runtime protection capabilities. Visibility alone is not sufficient as a defense. Verify that the platform provides runtime enforcement, not just logging or alerting, to protect sensitive data before it leaves your environment.
• Match platform type to your priorities. Browser-first tools may work well for web-heavy environments. Infrastructure-aligned options may appeal to teams already consolidating around a broader platform. More purpose-built platforms may suit organizations seeking AI-specific visibility, runtime defense, and agent governance in a single system.

Next Step Toward Securing Your AI Stack

The right LLM security tool is the one that matches how AI risk actually shows up in your environment. A browser extension tool won’t protect activity in native desktop apps. A SASE-integrated offering may introduce bundling trade-offs for teams with no existing stake in that vendor’s ecosystem. And visibility without runtime enforcement leaves the hardest problems unsolved. The clearest path forward is a platform that aligns with your deployment model, covers your full AI footprint, and enforces policies at the level of nuance your organization actually needs. WitnessAI is well-suited for organizations whose requirements include unified governance across human and digital workforce activities, network-level visibility, and protection extending to native apps, IDEs, and agent workflows. You can review the platform in more detail through its product overview. If it matches your operating model, book a demo to learn more.