Top AI Security Companies Protecting LLMs And AI Agents In 2027

Most companies now run large language models in production, and many have moved past chatbots to AI agents that call tools, read internal data and take actions on their own. That shift changes the security problem. A single prompt hidden in an email, a web page or a support ticket can redirect an agent, leak customer records or trigger a payment it was never meant to make. Traditional web application firewalls and endpoint tools were not built to read intent inside natural language, and they do not see what an agent does between the first prompt and the final action.

A new group of AI security companies has grown up to close that gap. Their platforms discover the models, agents and MCP servers running across an organization, scan models before deployment, test applications against adversarial prompts, and enforce policy at runtime with prompt injection defense and agent runtime security controls. Attackers are using AI too, as we cover in our guide to AI-powered threats to cybersecurity, so the window between a new attack technique and its use in the wild keeps shrinking.

This list focuses on platforms rather than consultancies. If you need people to attack your AI systems for you, see our list of AI red teaming companies. The ten vendors below are ranked on how much of the AI attack surface they cover, how well they handle agents specifically, and how practical they are to deploy.

AI Security Companies At A Glance

Company	Headquarters	Best For	Core Services
HiddenLayer	Austin, USA	Enterprises securing models, agents and coding agents in one platform	Model scanning, AI runtime security, attack simulation, AI discovery
Noma Security	New York, USA	Security teams that need to inventory and govern agents at scale	AI-SPM, agent access control, AI detection and response, AI red teaming
Zenity	New York, USA	Enterprises running agents in Microsoft Copilot Studio, Agentforce and other SaaS platforms	AISPM, agent boundaries, agentic identity, MCP security, AIDR
Palo Alto Networks Prisma AIRS	Santa Clara, USA	Existing Palo Alto Networks customers consolidating AI security	AI runtime security, model scanning, AI red teaming, AI posture management, agent security
Lasso Security	Tel Aviv, Israel	Teams that need low-latency guardrails without GPU infrastructure	AI discovery and AI-BOM, AI-SPM, automated red teaming, runtime enforcement
Pillar Security	Tel Aviv, Israel	Teams that want threat modeling, testing and guardrails in one loop	AI discovery, AI-SPM, red teaming, runtime guardrails, governance reporting
Straiker	Mountain View, USA	Enterprises deploying autonomous agents that need a runtime kill switch	Agent discovery, adversarial testing, runtime guardrails, agentic kill switch
WitnessAI	Mountain View, USA	Organizations governing employee AI use and agents together	AI observability, policy control, prompt injection blocking, runtime agent governance
Operant AI	San Francisco, USA	Cloud-native teams securing agents, APIs and MCP traffic in Kubernetes	AI Gatekeeper, MCP Gateway, semantic firewall, agent and API protection
Lakera	San Francisco, USA	Developers adding prompt injection defense to LLM apps through an API	AI agent security, workforce AI security, AI red teaming

Best AI Security Companies For 2027

1. HiddenLayer — Model Scanning Plus Agentic Runtime Security

HiddenLayer is an Austin-based AI security company whose platform is organized into four modules: AI Discovery, AI Supply Chain Security, AI Attack Simulation and AI Runtime Security. The supply chain module scans models before deployment for malicious code, backdoored weights and vulnerable dependencies, which matters for any team pulling open-weight models from public hubs. The runtime module works as an AI firewall for generative and agentic applications, enforcing guardrails against prompt injection, data leakage and unsafe behavior.

What stands out is how far HiddenLayer has moved into agents. Its Agentic Runtime Security product watches how agents behave in production and aims to stop manipulation, tool misuse and unauthorized actions, and in August 2026 it launched Agent Harness Security, which hooks into AI coding agents to block prompt injection and unsafe command execution. The company raised a $100 million Series B in September 2026 with Microsoft’s venture fund among the backers. It is a strong fit for large security teams that want model scanning and runtime protection from one vendor.

2. Noma Security — Agent Discovery, Access Control And Runtime Detection

Noma Security positions itself as an agent security and governance platform. Its AI-SPM module discovers agents, MCP servers, skills and tools across the organization and surfaces the risks attached to each one. Agent Access Control lets security teams approve or block agents, MCP servers and skills, and scope what each agent can do by user, team or tool sensitivity. AI-DR monitors agent behavior at runtime, and an AI red teaming module runs multi-turn attacks against agents and AI applications.

Noma is a good fit when the first problem is visibility: many companies do not know how many agents their staff have already built in SaaS platforms and internal tools. The company lists its office in New York, runs research and development in Tel Aviv, and raised a $100 million Series B in 2025 to expand in North America and EMEA. Buyers who want AI security posture management, identity-style controls for agents and runtime detection under one console should put Noma on their shortlist.

3. Zenity — Security For Agents Built In Copilot Studio And Agentforce

Zenity secures AI agents across SaaS, cloud and endpoint environments, with explicit support for Microsoft Copilot Studio, Microsoft 365 Copilot and Salesforce Agentforce. Its platform combines AI observability, AI security posture management, exposure management, runtime boundaries, agentic identity and access management, MCP security and AI detection and response. The idea is to govern what an agent is built to do, what it can reach and what it actually does.

Zenity’s detection is intent based: it examines the full execution path, including tool calls, memory access and data use, so it can catch a harmful outcome even when the input looked harmless. Its research arm, Zenity Labs, has disclosed vulnerabilities in agent frameworks, including a zero-click attack class it named AgentFlayer. The company raised a $125 million Series C in August 2026, runs its commercial operations from New York and its R&D from Tel Aviv. It suits enterprises where business users build agents in low-code platforms faster than security can review them.

4. Palo Alto Networks Prisma AIRS — Full-Lifecycle AI Security Built On The Protect AI Acquisition

Prisma AIRS is the AI security platform from Palo Alto Networks, which acquired the model security specialist Protect AI in July 2025. The platform covers AI supply chain security that scans third-party models for tampering, malicious scripts and deserialization attacks; AI red teaming that simulates attacks against agents and applications; AI runtime security that enforces real-time safeguards against manipulation and data exposure; and AI posture management for models, agents and the data they use.

Prisma AIRS also includes dedicated AI agent security for risks such as memory manipulation and tool misuse, plus agent identity and agentic endpoint coverage. Its main advantage is integration: organizations already running Palo Alto Networks firewalls, Prisma Cloud or Cortex can add AI coverage without onboarding a new vendor or a new console. The tradeoff is that smaller teams may find a specialist vendor faster to deploy and easier to tune. For large enterprises standardizing on one security platform, it is the most complete option from an established vendor.

5. Lasso Security — CPU-Only Guardrails With Intent-Based Agent Monitoring

Lasso Security is a Tel Aviv-based company with an office in New York. Its platform covers AI discovery and inventory with an AI bill of materials, AI security posture management, automated AI red teaming with an attack library of more than 3,000 techniques, runtime enforcement, and AI detection and response. Runtime policies can be enforced inline at the proxy, API or AI gateway layer, and an Intent Security module looks for behavioral anomalies in how agents act.

Lasso’s most distinctive work is on guardrail performance. In September 2026 it launched LEAP, a transformer-free guardrail that runs on ordinary server processors without a GPU and, by the company’s own benchmarks, returns a verdict in under five milliseconds. A second self-hosted engine handles the smaller share of decisions that need judgment against plain-language policies. That design suits teams that want to inspect every prompt and tool call without adding noticeable latency or GPU cost. It also covers shadow AI use by employees, which makes it useful beyond custom applications.

6. Pillar Security — Lifecycle Platform From Threat Modeling To Runtime Guardrails

Pillar Security is a Tel Aviv company founded in 2023 by Dor Sarig and Ziv Karliner. Its platform aims to discover, test, secure and govern every AI agent in an organization. It inventories agents, models, MCP servers, skills and coding agents, including unsanctioned shadow AI, and its AI-SPM scans agent configurations, permissions and identities for exposures. Red teaming runs multi-turn adversarial attacks and returns transcripts and reproduction steps, while runtime guardrails detect malicious intent and mask, block or log sensitive data.

Pillar stands out for treating the AI lifecycle as a feedback loop. An AI Workbench supports threat modeling before code is written, red team findings feed the guardrails, and governance reports map controls to OWASP, MITRE ATLAS, NIST and the EU AI Act. It can run in the cloud, self-hosted or in a hybrid setup, which helps regulated buyers who cannot send prompts to a third-party service. It is a good match for product security teams building their own LLM features.

7. Straiker — Agent Testing And Runtime Defense With An Agentic Kill Switch

Straiker is an agentic AI security company headquartered in Mountain View, California, with an office in Bengaluru. Its platform has three products. Discover AI finds agents, tools and MCP servers and monitors their posture and misconfigurations. Ascend AI runs adversarial tests against agents for prompt injection, goal hijacking, tool misuse and inter-agent manipulation before release. Defend AI provides runtime guardrails that detect and block identity abuse, memory poisoning, data exfiltration and resource exploitation.

The feature buyers ask about most is the agentic kill switch, which lets a security team stop an agent that has gone off course. Straiker also publishes solution pages for securing Claude and ChatGPT deployments. Founded in 2024 by Ankur Shah and Sreenath Kurupati, it raised a $64 million Series A in June 2026 with Citi Ventures and Workday Ventures participating. It is a good fit for organizations moving agents from pilot into workflows that touch money, customer data or production systems.

8. WitnessAI — Observe, Control And Protect For Employees And Agents

WitnessAI, based in Mountain View, California, secures and governs AI use by both employees and AI agents. Its platform is built around three areas. Observe discovers AI use, catalogs AI apps and MCP servers, and shows interactions and agent activity in real time. Control sets policies by department, role or intent, decides which MCP servers and tools agents can use, routes prompts to models by risk or cost, and redacts sensitive data. Protect blocks prompt injections and jailbreaks, filters harmful outputs and governs agent actions at runtime.

WitnessAI is a practical choice when the security team owns both problems: staff pasting data into public chatbots and developers shipping agents. Having one policy engine for both avoids running separate tools for shadow AI and application security. Its model routing control is also useful for teams that want cheaper models for low-risk tasks and stricter handling for sensitive prompts. Mid-sized and large enterprises with a mix of sanctioned and unsanctioned AI use will get the most from it.

9. Operant AI — MCP Gateway And Semantic Firewall For Agent Traffic

Operant AI, built in San Francisco, focuses on real-time security for AI systems, agents, APIs and MCP deployments. AI Gatekeeper protects AI applications and agents in real time, maps the live API environment and blocks OWASP Top 10 API attacks without requiring VPC traffic mirroring. The MCP Gateway gives visibility and control over MCP servers, clients, tools and connections, with registries, allow and deny lists, and access controls for non-human identities.

Its Semantic Firewall inspects agent actions inline and can permit, deny or mask them, and separate Agent Protector, Endpoint Protector and API and Cloud Protector products extend coverage to devices and Kubernetes-native environments. That runtime, network-level approach suits platform and DevSecOps teams who want to enforce policy where agent traffic actually flows instead of inside each application. For a deeper look at the MCP layer specifically, compare it with the tools in our list of MCP security tools.

10. Lakera — API-First Prompt Attack Prevention Backed By Check Point

Lakera became part of Check Point Software in late 2025 and continues to sell under its own name, with offices and R&D in Zurich and San Francisco. Its AI Agent Security product provides runtime protection, real-time threat detection, prompt attack prevention and data leakage protection for LLM applications and agents. Workforce AI Security covers shadow AI discovery across apps and browsers with data protection in prompts, and AI Red Teaming tests GenAI systems with direct and indirect attack simulations.

Lakera is known for its API-first, cloud-native design and quotes runtime latency under 50 milliseconds across more than 100 languages, so it is easy to drop into an existing application. Its threat intelligence draws on Gandalf, the public prompt injection game, which has gathered a large body of real adversarial prompts. Teams that want fast prompt injection defense without rebuilding their stack, and Check Point customers who want AI coverage that will tie into their existing vendor, are the best fit.

How To Choose An AI Security Platform

The vendors on this list overlap, but they start from different places. These questions help narrow the field before you book demos.

Do We Need To Secure Employee AI Use, Our Own AI Apps, Or Both?

Some platforms focus on what staff paste into public chatbots and which AI tools they install. Others focus on applications and agents your developers build. If you have both problems, look for a single policy engine that covers both, such as WitnessAI, Lasso or Lakera, rather than buying two separate products that disagree about what counts as sensitive data.

How Does The Platform Handle Agents, Not Just Prompts?

Prompt filtering alone does not stop an agent that has been redirected by poisoned content in a document or tool response. Ask each vendor to show how it tracks tool calls, memory access and actions across a full agent session, whether it can block an action before it executes, and whether it offers a way to stop an agent entirely. Agent runtime security is where the products differ most.

What Latency And Deployment Options Are Available?

Inline guardrails sit in the request path, so every millisecond counts for customer-facing apps. Ask for latency figures measured on your own traffic, and check whether the platform can run self-hosted or in your cloud account if your data cannot leave your environment. CPU-only options can also cut infrastructure cost.

Does It Cover The Model Supply Chain And MCP Servers?

If your teams download open-weight models or connect agents to third-party MCP servers, you need scanning and an allow list before those components reach production. Ask whether the vendor scans model files for malicious code and whether it can inventory and restrict MCP servers. Our guide to the best MCP servers shows how quickly that ecosystem is growing.

How Will Findings Reach Our Existing Security Stack?

An AI security platform is only useful if alerts land where analysts already work. Confirm integrations with your SIEM, SOAR and ticketing tools, and check that AI security posture management findings map to frameworks your auditors recognize, such as OWASP, MITRE ATLAS and NIST.

Conclusion

AI agents have turned prompt injection from a curiosity into a path to real data loss and unauthorized actions. The AI security companies on this list give security teams the visibility, testing and runtime controls that general security tools lack. Start by inventorying the models and agents already in use, decide whether employee AI use or custom agents is the bigger risk, and then run a proof of concept with two or three vendors against your own applications before committing.