Enterprise AI has graduated. It has moved from isolated prototypes to systems that shape real decisions: drafting customer responses, summarizing internal knowledge, and powering agent workflows that trigger actions in business systems. This creates a new security surface—one that sits right between your people, your proprietary data, and automated execution.
What Counts as an “AI Security Tool”?
While “AI Security” is a broad umbrella, operational tools in 2026 generally fall into specific functional buckets:
- Discovery & Governance: Identifying who is using AI and where.
- Runtime Protection: Enforcing guardrails in real-time (blocking prompt injections or data leaks).
- Red Teaming: Testing models against adversarial attacks before deployment.
- Supply Chain Security: Assessing risks in the models and packages you import.
- SaaS & Identity: Managing permissions where AI lives inside your existing apps.
Top 10 AI Security Tools for Enterprises in 2026
1. Koi
Best for: Endpoint & Software Control
Koi approaches security from the software control layer. It helps enterprises govern what gets installed on endpoints—including browser extensions and developer assistants. This is critical because “Shadow AI” often enters through harmless-looking tools. Koi turns ad-hoc installs into a governed process, preventing unauthorized data exposure at the source.
2. Noma Security
Best for: Pipeline Governance
Noma is the choice for securing the entire AI lifecycle across teams. It focuses on discovery and inventory, mapping which apps touch sensitive data. It’s particularly strong for organizations with multiple business units deploying different models, ensuring consistent oversight without slowing down development.
3. Aim Security
Best for: GenAI Adoption & Employee Use
Aim targets the “use layer”—where employees interact with AI tools. It creates visibility into usage patterns and enforces policy to prevent sensitive data exposure. Aim is ideal for companies where the biggest risk isn’t a custom model, but the workforce pasting proprietary info into public chatbots.
4. Mindgard
Best for: Red Teaming & Testing
Mindgard pressure-tests your AI before it goes live. It specializes in identifying vulnerabilities in RAG (Retrieval-Augmented Generation) and agent workflows. By simulating adversarial attacks—like jailbreaks or injection techniques—it helps engineering teams fix weak points early in the release cycle.
5. Protect AI
Best for: Supply Chain Security
For enterprises building their own AI, Protect AI secures the ingredients. It focuses on the risks inherited from external models, datasets, and libraries. It provides a platform to standardize security practices across the development lifecycle, closing the gap between “build” and “secure.”
6. Radiant Security
Best for: SOC Automation
Radiant uses AI to protect against AI. It focuses on security operations, using agentic automation to triage alerts and guide response actions. It helps analysts cut through the noise generated by new SaaS events and integration signals, keeping humans in the loop only when necessary.
7. Lakera
Best for: Runtime Guardrails
Lakera sits at the inference layer. It is designed to block prompt injections and sensitive data leaks in real-time. This is essential for applications exposed to untrusted inputs, such as customer-facing support bots, ensuring the model is constrained to safe behaviors.
8. CalypsoAI
Best for: Centralized Policy Enforcement
CalypsoAI provides a central control point for inference protection. It is valuable for enterprises running multiple models, allowing them to apply a single set of security policies across all apps. It prevents unsafe decisions or tool use when model outputs become workflow inputs.
9. Cranium
Best for: Compliance & Risk Management
Cranium excels at governance and inventory. It answers the question: “What AI do we have, and who owns it?” It is particularly relevant for regulated industries that need to provide evidence of risk management and maintain continuous oversight of their AI footprint.
10. Reco
Best for: SaaS Identity & Shadow IT
Since so much AI exposure happens inside SaaS platforms, Reco focuses on the surrounding risks: permissions, risky integrations, and account takeovers. It secures the identity layer, ensuring that AI features inside your existing tools don’t inadvertently expose files or data.
Why AI Security Matters Now
AI risks don’t behave like traditional software bugs. They introduce three unique challenges:
- Systematic Leakage: A single bad prompt can expose sensitive context. Multiplied across thousands of interactions, leakage becomes systematic.
- Manipulable Instructions: AI systems can be tricked by malicious inputs or “indirect injections” from retrieved documents, steering workflows into unsafe actions.
- Agent Blast Radius: When AI can execute code or trigger actions, a mistake isn’t just “wrong text”—it’s “wrong action.” This expands the potential damage significantly.
How to Choose the Right Tool
Avoid buying a generic “platform.” Instead, map your specific AI footprint:
- Employee-driven? Look for browser and endpoint controls (Koi, Aim).
- Building internal apps? Prioritize supply chain and testing (Protect AI, Mindgard).
- Production agents? You need runtime guardrails (Lakera, CalypsoAI).
The best security program isn’t about blocking AI; it’s about building a sustainable loop of discovery, governance, and enforcement that allows your business to innovate safely.
