7 AI Detection and Response Platforms for Enterprise Security Teams

The most difficult AI incidents do not necessarily begin with an obviously malicious prompt.

An employee can ask an approved agent to summarize customer data. The agent retrieves the correct records, invokes an approved tool, generates the requested output, and then sends it somewhere it should never have gone. Every individual action may look legitimate in isolation. The incident only becomes visible when security can reconstruct the entire sequence and understand what the user intended, what the agent inferred, which systems it touched, and where the execution path diverged.

That is the problem AI Detection and Response, or AIDR, is beginning to solve.

The AIDR Problem Starts Where Conventional Telemetry Loses Meaning

Traditional security products are very good at answering event-oriented questions.

• Which process executed?

• Which identity authenticated?

• Which API was called?

• Which endpoint contacted a domain?

• Which database received a query?

Those signals remain important when AI agents are involved, but they no longer explain enough. An AI agent can legitimately perform each of those actions while still producing an unacceptable outcome. That is because agentic risk often exists between events.

An agent might read a document, interpret embedded text as instruction, use that instruction to select a tool, query sensitive information, transform the result, and send it through another authorized service. A conventional security stack may record every step without recognizing the chain as one AI-driven incident.

For enterprise teams, that means evaluating AI Detection and Response platforms according to the security question they can answer, not merely the number of AI-specific detections they advertise.

7 AI Detection and Response Platforms for Enterprise Security Teams

1. Dash Security — AIDR Built Around the Complete Agentic Session

Dash Security takes one of the most distinctly agent-centric approaches in the AIDR market.

Rather than positioning the prompt, model, endpoint, or individual tool call as the primary security object, Dash focuses on the agentic session. Its platform follows an agent across environments and captures what the user intended, how the agent reasoned, what it actually did, which actors and systems participated, and where execution diverged from the original intent.

A security analyst investigating an ordinary endpoint event may already know what constitutes suspicious behavior. Agent activity is more ambiguous. Reading a repository could be expected. Executing a shell command could be expected. Accessing a ticket could be expected. The meaningful question is whether those actions belong to the job the agent was supposed to perform.

Dash uses intent similarity and intent-drift analysis to supply that missing context. The platform can connect user intent with subsequent tool use, commands, data access, and agent actions across a full session rather than evaluating events independently. This allows security teams to identify situations in which technically authorized actions accumulate into behavior that is no longer aligned with the user's objective.

Response is graduated rather than purely binary. Depending on context, Dash can warn a user, request human approval, block an action, restrict activity, remediate an exposure, suspend a session, or send enriched detections into existing security operations systems.

This makes Dash particularly relevant for enterprises that do not want AIDR to become another standalone alert console. The company explicitly positions the platform alongside existing EDR, SIEM, DLP, IdP, ticketing, and collaboration systems rather than as a replacement for them.

Where Dash is strongest

• Full agentic-session reconstruction

• User-intent and intent-drift analysis

• Known and shadow agent discovery

• MCP, skill, plugin, and tool visibility

• Agentic supply-chain context

• AI DLP

• Runtime guardrails

• Human-in-the-loop response

• Cross-environment agent tracking

• Security-stack integration

2. Noma Security — Joining Posture, Identity, and Runtime Behavior

Noma Security approaches AIDR from the premise that runtime behavior becomes much more useful when it is enriched with everything security already knows about the agent.

Its Noma AI-DR product monitors agent behavior across full sessions rather than inspecting single prompts alone. The platform evaluates prompts, responses, tool calls, tool responses, skills, agent-to-agent interactions, identity, accessible data, and behavioral baselines when deciding whether an action is risky.

Its runtime controls cover direct and indirect prompt injection, data leakage, malicious intent, tool poisoning, scope violations, and other agentic behavior. Individual detectors can be configured to monitor, alert, mask information, block activity, or route a decision to a human.

Noma also extends the same governance model across homegrown agents, SaaS agents, endpoint coding tools, cloud AI environments, and MCP infrastructure. Policies can define which tools and servers agents may use and can be associated with user identities or enterprise groups.

3. Cisco AI Defense — Extending Enterprise Security Controls Into Agent Runtime

Cisco AI Defense brings a different advantage: AI runtime protection attached to a much broader enterprise security environment.

Cisco expanded AI Defense with Agent Runtime Protection designed to inspect LLM and MCP interactions across chat applications, agent frameworks, and managed agent runtimes. The company specifically frames the problem around agents that execute code, send messages, query databases, browse the web, and act through external tools.

An AI agent may generate the risky decision, but the consequences still appear in familiar enterprise systems: data services, applications, cloud infrastructure, networks, endpoints, APIs, and identities. Cisco's broader position allows organizations to connect AI-specific inspection with a security architecture already responsible for many of those downstream systems.

Agent Runtime Protection can inspect interactions at the point where models and agents communicate with external capabilities. That provides opportunities to detect or prevent harmful requests before they become downstream actions.

Cisco therefore fits particularly well into organizations where AIDR is expected to become another control layer inside a mature enterprise security program rather than a greenfield AI-specific stack.

4. HiddenLayer — Runtime Investigation for AI-Native Threats

HiddenLayer is one of the vendors most explicitly aligned with the term AI Detection and Response.

Its platform originated around protecting AI models and generative AI systems and has expanded into agentic runtime security. HiddenLayer's 2026 runtime capabilities include Agentic Runtime Visibility, Agentic Investigation & Threat Hunting, and Agentic Detection & Enforcement.

Some AI security tools are optimized primarily around inline blocking. HiddenLayer also focuses on what analysts need after suspicious behavior has been detected: visibility into agent interactions, the ability to reconstruct execution, and AI-specific threat hunting.

Its AIDR capabilities monitor agent activity and system interactions to identify anomalous behavior. HiddenLayer also protects against data exposure, prompt injection, manipulation of knowledge sources, and other threats that can influence an agent before or during execution.

5. Pillar Security — Detection and Response Across Agentic Engineering Workflows

Pillar Security deserves attention because the AI attack surface is moving directly into software delivery.

Coding agents increasingly read repositories, execute shell commands, modify files, create commits, interact with pull requests, access CI credentials, and execute tasks inside build environments. That makes development pipelines one of the highest-impact environments for autonomous AI.

Pillar has expanded its agent-security approach into agentic CI/CD, including discovery and posture management for agents operating inside pipelines and runtime protection extending from developer workstations into CI/CD workflows. Repository content that security tools historically treated as passive data can influence an agent that has permission to execute commands or push code.

Pillar's value lies in following the relationship between AI assets, agent configurations, tools, permissions, and runtime behavior through the engineering lifecycle. Earlier versions of the platform have also emphasized agent discovery, MCP and tool cataloging, automated red teaming, and lifecycle security.

6. Protect AI — Runtime Protection Connected to AI Security Testing

Protect AI approaches the runtime problem from an application-security perspective.

Its AI security portfolio has included Recon for adversarial testing and Layer for LLM runtime protection, allowing organizations to connect pre-production validation with controls designed to identify and mitigate threats during live AI interactions. That connection is valuable because AIDR should not begin only after production deployment.

Red-team exercises frequently reveal the particular ways an application can be manipulated: prompt injection, unsafe handling of sensitive information, policy bypasses, insecure retrieval behavior, or undesirable model responses. The strongest operating model turns those discoveries into runtime protections.

Protect AI's broader heritage in machine-learning security also gives it context beyond the runtime interaction itself. The company has addressed AI supply-chain security, model scanning, open source ML risks, and adversarial testing, which can matter for enterprises that need to secure AI systems across build and production.

7. CalypsoAI — Runtime Guardrails With Strong Observability

CalypsoAI brings together runtime guardrails and observability for enterprises deploying AI systems into sensitive operational environments.

Its runtime security model is designed to inspect interactions, detect prompt injection and sensitive-data risks, enforce policies, and give security teams visibility into AI behavior. CalypsoAI has positioned these controls across use cases including fraud detection, security operations, workplace AI, and other environments in which model decisions may directly influence business processes. The observability component is particularly relevant to AIDR.

Blocking obviously malicious content is only one part of enterprise response. Analysts also need historical evidence showing how AI was used, which policies triggered, whether certain applications consistently approach risk boundaries, and where unusual behavior is concentrated.

This kind of operational record becomes increasingly important as organizations deploy AI across business units with different risk tolerances.