Enterprise AI security is not one product category. The risks begin when employees install browser extensions or developer assistants, continue through models, datasets, and retrieval pipelines, and become more consequential when agents can access files or trigger business actions.
A 2026 AI News overview names ten vendors across that landscape: Koi, Noma Security, Aim Security, Mindgard, Protect AI, Radiant Security, Lakera, CalypsoAI, Cranium, and Reco.
The useful conclusion is not that every enterprise needs all ten. It is that organisations should map their actual AI footprint before choosing the security layers that fit it.
Discovery Comes Before Effective Enforcement
Shadow AI often arrives through ordinary software. An employee installs an extension, an IDE assistant gains repository access, or a SaaS platform enables an embedded copilot. Security teams cannot govern tools they do not know exist.
Koi focuses on software intake across endpoints, including packages and extensions. Aim Security looks at workforce generative-AI use and data exposure. Noma Security and Cranium emphasise inventories, ownership, application risk, and governance across decentralised AI environments.
Reco approaches the problem through SaaS identity, permissions, exposed files, integrations, and configuration drift. This matters because AI frequently inherits the access already granted to a business application rather than creating an obvious new model endpoint.
Discovery should produce an actionable inventory: owner, purpose, users, data, model, integrations, permissions, risk tier, and current status. A dashboard full of unnamed activity is visibility without accountability.
Build-Time and Runtime Controls Address Different Failures
Mindgard provides adversarial testing and red teaming for prompt injection, jailbreaks, retrieval manipulation, and unsafe tool use. Protect AI extends attention to models, packages, datasets, and other supply-chain components used during development.
These controls help teams find weaknesses before release, but production introduces changing prompts, documents, permissions, and attackers. Lakera and CalypsoAI focus on inference-time guardrails that can detect manipulation, limit sensitive-data exposure, and constrain how applications or agents behave.
Runtime protection is particularly important for retrieval-augmented systems. A legitimate user may ask an innocent question while a retrieved document contains hidden instructions intended to redirect the model. The application must treat external content as data rather than trusted authority.
Agents raise the stakes from unsafe text to unsafe execution. Least-privilege access, tool restrictions, approval for consequential actions, rate limits, and complete logs must accompany content filters. A guardrail that blocks rude language but permits an agent to alter the wrong account protects the least important boundary.
Security Operations Need a Repeatable Control Loop
Radiant Security focuses on agentic assistance for security operations, automating alert triage and guiding investigations. Such tools can reduce analyst overload as AI introduces new SaaS, identity, data, and application signals.
Automation inside the security team needs the same transparency demanded elsewhere. Analysts should see why an event was prioritised, which evidence supports the recommendation, and what action the system proposes. High-impact response should remain subject to clear human control.
A mature program connects discovery, policy, enforcement, testing, monitoring, response, and evidence. Low-risk employee use may need lightweight guidance, while an agent with sensitive data and write access requires strong technical controls and continuous evaluation.
Tool selection should mirror real workflows. Enterprises can test sensitive prompts, indirect injection through retrieved files, differences between user and administrator permissions, and an agent requesting elevated access. Integrations with identity, ticketing, SIEM, and data governance matter more than a long feature list.
The source’s ranking places Koi first, but enterprises should treat any universal order cautiously. Vendor fit depends on whether risk is concentrated in workforce use, production applications, agent execution, supply chains, or SaaS permissions.
AI security succeeds when controls survive beyond a pilot and teams can show what is allowed, what happened, and why. The right architecture is rarely one protective shell. It is a layered system that discovers change, applies proportionate rules, tests assumptions, and limits the damage when an intelligent component behaves in an unintelligent way.
