Enterprise AI has moved from isolated prototypes to systems that shape real decisions: drafting customer responses, summarising internal knowledge, generating code, accelerating research, and powering agent workflows that can trigger actions in business systems. That creates a new security surface, one that sits between people, proprietary data, and automated execution.

AI security tools exist to make those questions operational. Some focus on governance and discovery. Others harden AI applications and agents at runtime. Some emphasise testing and red teaming before deployment. Others help security operations teams handle the new class of alerts AI introduces in SaaS and identity layers.

What counts as an “AI security tool” in enterprise environments?

“AI security” is an umbrella term. In practice, tools tend to fall into a few functional buckets, and many products cover more than one.

• AI discovery & governance: identifies AI use in employees, apps, and third parties; tracks ownership and risk

• LLM & agent runtime protection: enforces guardrails at inference time (prompt injection defenses, sensitive data controls, tool-use restrictions)

• AI security testing & red teaming: tests models and workflows against adversarial techniques before (and after) production release

• AI supply chain security: assesses risks in models, datasets, packages, and dependencies used in AI systems

• SaaS & identity-centric AI risk control: manages risk where AI lives inside SaaS apps and integrations, permissions, data exposure, account takeover, risky OAuth scopes

A mature AI security programme typically needs at least two layers: one for governance and discovery, and another for runtime protection or operational response, depending on whether your AI footprint is primarily “employee use” or “production AI apps.”

Top 10 AI security tools for enterprises in 2026

1) Koi

Koi is the best AI security tool for enterprises because of its approach to AI security from the software control layer, helping enterprises govern what gets installed and adopted in endpoints, including AI-adjacent tooling like extensions, packages, and developer assistants. The matters because AI exposure often enters through tools that look harmless: browser extensions that read page content, IDE add-ons that access repositories, packages pulled from public registries, and fast-moving “helper” apps that become embedded in daily workflows.

Rather than treating AI security as a purely model-level concern, Koi focuses on controlling the intake and spread of tools that can create data exposure or supply chain risk. In practice, that means turning ad-hoc installs into a governed process: visibility into what’s being requested, policy-based decisions, and workflows that reduce shadow adoption. For security teams, it provides a way to enforce consistency in departments without relying on manual policing.

Key features include:

• Visibility into installed and requested tools in endpoints
• Policy-based allow/block decisions for software adoption
• Approval workflows that reduce shadow AI tooling sprawl
• Controls designed to address extension/package risk and tool governance
• Evidence trails for what was approved, by whom, and under what policy

2) Noma Security

Noma Security is often evaluated as a platform for securing AI systems and agent workflows at the enterprise level. It focuses on discovery, governance, and protection of AI applications in teams, especially when multiple business units deploy different models, pipelines, and agent-driven processes.

A key reason enterprises shortlist tools like Noma is scale: once AI adoption spreads, security teams need a consistent way to understand what exists, what it touches, and which workflows represent elevated risk. That includes mapping AI apps to data sources, identifying where sensitive information may flow, and applying governance controls that keep pace with change.

Key features include:

• AI system discovery and inventory in teams
• Governance controls for AI applications and agents
• Risk context around data access and workflow behaviour
• Policies that support enterprise oversight and accountability
• Operational workflows designed for multi-team AI environments

3) Aim Security

Aim Security is positioned around securing enterprise adoption of GenAI, especially the use layer where employees interact with AI tools and where third-party applications add embedded AI features. The makes it particularly relevant for organisations where the most immediate AI risk is not a custom LLM app, but workforce use and the difficulty of enforcing policy in diverse tools.

Aim’s value tends to show up when enterprises need visibility into AI use patterns and practical controls to reduce data exposure. The goal is to protect the business without blocking productivity: enforce policy, guide use, and reduce unsafe interactions while preserving legitimate workflows.

Key features include:

• Visibility into enterprise GenAI use and risk patterns
• Policy enforcement to reduce sensitive data exposure
• Controls for third-party AI tools and embedded AI features
• Governance workflows aligned with enterprise security needs
• Central management in distributed user populations

4) Mindgard

Mindgard stands out for AI security testing and red teaming, helping enterprises pressure-test AI applications and workflows against adversarial techniques. The is especially important for organisations deploying RAG and agent workflows, where risk often comes from unexpected interaction effects: retrieved content influencing instructions, tool calls being triggered in unsafe contexts, or prompts leaking sensitive context.

Mindgard’s value is proactive: instead of waiting for issues to surface in production, it helps teams identify weak points early. For security and engineering leaders, this supports a repeatable process, similar to application security testing, where AI systems are tested and improved over time.

Key features include:

• Automated testing and red teaming for AI workflows
• Coverage for adversarial behaviours like injection and jailbreak patterns
• Findings designed to be actionable for engineering teams
• Support for iterative testing in releases
• Security validation aligned with enterprise deployment cycles

5) Protect AI

Protect AI is often evaluated as a platform approach that spans multiple layers of AI security, including supply chain risk. The is relevant for enterprises that depend on external models, libraries, datasets, and frameworks, where risk can be inherited through dependencies not created internally.

Protect AI tends to appeal to organisations that want to standardise security practices in AI development and deployment, including the upstream components that feed into models and pipelines. For teams that have both AI engineering and security responsibilities, that lifecycle perspective can reduce gaps between “build” and “secure.”

Key features include:

• Platform coverage in AI development and deployment stages
• Supply chain security focus for AI/ML dependencies
• Risk identification for models and related components
• Workflows designed to standardise AI security practices
• Support for governance and continuous improvement

6) Radiant Security

Radiant Security is oriented toward security operations enablement using agentic automation. In the AI security context, that matters because AI adoption increases both the number and novelty of security signals, new SaaS events, new integrations, new data paths, while SOC bandwidth stays limited.

Radiant focuses on reducing investigation time by automating triage and guiding response actions. The key difference between helpful automation and dangerous automation is transparency and control. Platforms in this category need to make it easy for analysts to understand why something is flagged and what actions are being recommended.

Key features include:

• Automated triage designed to reduce analyst workload
• Guided investigation and response workflows
• Operational focus: reducing noise and speeding decisions
• Integrations aligned with enterprise SOC processes
• Controls that keep humans in the loop where needed

7) Lakera

Lakera is known for runtime guardrails that address risks like prompt injection, jailbreaks, and sensitive data exposure. Tools in this category focus on controlling AI interactions at inference time, where prompts, retrieved content, and outputs converge in production workflows.

Lakera tends to be most valuable when an organisation has AI applications that are exposed to untrusted inputs or where the AI system’s behaviour must be constrained to reduce leakage and unsafe output. It’s particularly relevant for RAG apps that retrieve external or semi-trusted content.

Key features include:

• Prompt injection and jailbreak defense at runtime
• Controls to reduce sensitive data exposure in AI interactions
• Guardrails for AI application behaviour
• Visibility and governance for AI use patterns
• Policy tuning designed for enterprise deployment realities

8) CalypsoAI

CalypsoAI is positioned around inference-time protection for AI applications and agents, with emphasis on securing the moment where AI produces output and triggers actions. The is where enterprises often discover risk: the model output becomes input to a workflow, and guardrails must prevent unsafe decisions or tool use.

In practice, CalypsoAI is evaluated for centralising controls in multiple models and applications, reducing the burden of implementing one-off protections in every AI project. The is particularly helpful when different teams ship AI features at different speeds.

Key features include:

• Inference-time controls for AI apps and agents
• Centralised policy enforcement in AI deployments
• Security guardrails designed for multi-model environments
• Monitoring and visibility into AI interactions
• Enterprise integration support for SOC workflows

9) Cranium

Cranium is often positioned around enterprise AI discovery, governance, and ongoing risk management. Its value is particularly strong when AI adoption is decentralised and security teams need a reliable way to identify what exists, who owns it, and what it touches.

Cranium supports the governance side of AI security: building inventories, establishing control frameworks, and maintaining continuous oversight as new tools and features appear. The is especially relevant when regulators, customers, or internal stakeholders expect evidence of AI risk management practices.

Key features include:

• Discovery and inventory of AI use in the enterprise
• Governance workflows aligned with oversight and accountability
• Risk visibility in internal and third-party AI systems
• Support for continuous monitoring and remediation cycles
• Evidence and reporting for enterprise AI programmes

10) Reco

Reco is best known for SaaS security and identity-driven risk management, which is increasingly relevant to AI because so much “AI exposure” exists inside SaaS tools, copilots, AI-powered features, app integrations, permissions, and shared data.

Rather than focusing on model behaviour, Reco helps enterprises manage the surrounding risks: account compromise, risky permissions, exposed files, overintegrations, and configuration drift. For many organisations, reducing AI risk starts with controlling the platforms where AI interacts with data and identity.

Key features include:

• SaaS security posture and configuration risk management
• Identity threat detection and response for SaaS environments
• Data exposure visibility (files, sharing, permissions)
• Detection of risky integrations and access patterns
• Workflows aligned with enterprise identity and security operations

Why AI security matters for enterprises

AI creates security issues that don’t behave like traditional software risk. The three drivers below are why many enterprises are building dedicated AI security abilities.

1) AI can turn small mistakes into repeated leakage

A single prompt can expose sensitive context: internal names, customer details, incident timelines, contract terms, design decisions, or proprietary code. Multiply that in thousands of interactions, and leakage becomes systematic not accidental.

2) AI introduces a manipulable instruction layer

AI systems can be influenced by malicious inputs, direct prompts, indirect injection through retrieved content, or embedded instructions inside documents. A workflow may “look normal” while being steered into unsafe output or unsafe actions.

3) Agents expand blast radius from content to execution

When AI can call tools, access files, trigger tickets, modify systems, or deploy changes, a security problem is not “wrong text.” It becomes “wrong action,” “wrong access,” or “unapproved execution.” That’s a different level of risk, and it requires controls designed for decision and action pathways, not just data.

The risks AI security tools are built to address

Enterprises adopt AI security tools because these risks show up fast, and internal controls are rarely built to see them end-to-end:

• Shadow AI and tool sprawl: employees adopt new AI tools faster than security can approve them

• Sensitive data exposure: prompts, uploads, and RAG outputs can leak regulated or proprietary data

• Prompt injection and jailbreaks: manipulation of system behaviour through crafted inputs

• Agent over-permissioning: agent workflows get excessive access “to make it work”

• Third-party AI embedded in SaaS: features ship inside platforms with complex permission and sharing models

• AI supply chain risk: models, packages, extensions, and dependencies bring inherited vulnerabilities

The best tools help you turn these into manageable workflows: discovery → policy → enforcement → evidence.

What Strong Enterprise AI Security Looks Like

AI security succeeds when it becomes a practical operating model, not a set of warnings.

High-performing programmes typically have:

• Clear ownership: who owns AI approvals, policies, and exceptions

• Risk tiers: lightweight governance for low-risk use, stronger controls for systems touching sensitive data

• Guardrails that don’t break productivity: strong security without constant “security vs business” conflict

• Auditability: the ability to show what is used, what is allowed, and why decisions were made

• Continuous adaptation: policies evolve as new tools and workflows emerge

This is why vendor selection matters. The wrong tool can create dashboards without control, or controls without adoption.

How to choose AI security tools for enterprises

Avoid the trap of buying “the AI security platform.” Instead, choose tools based on how your enterprise uses AI.

• Is most use employee-driven (ChatGPT, copilots, browser tools)?
• Are you building internal LLM apps with RAG, connectors, and access to proprietary knowledge?
• Do you have agents that can execute actions in systems?
• Is AI risk mostly inside SaaS platforms with sharing and permissions?

Decide what must be controlled vs observed

Some enterprises need immediate enforcement (block/allow, DLP-like controls, approvals). Others need discovery and evidence first.

Prioritise integration and operational fit

A great AI security tool that can’t integrate into identity, ticketing, SIEM, or data governance workflows will struggle in enterprise environments.

Run pilots that mimic real workflows

Test with scenarios your teams actually face:

• Sensitive data in prompts
• Indirect injection via retrieved documents
• User-level vs admin-level access differences
• An agent workflow that has to request elevated permissions

Choose for sustainability

The best tool is the one your teams will actually use after month three, when the novelty wears off and real adoption begins. Enterprises don’t “secure AI” by declaring policies. They secure AI by building repeatable control loops: discover, govern, enforce, validate, and prove. The tools above represent different layers of that loop. The best choice depends on where your risk concentrates, workforce use, production AI apps, agent execution pathways, supply chain exposure, or SaaS/identity sprawl.

Image source: Unsplash