AI agents do not fail the way applications used to. An agent can retrieve a document, decide on a plan, call a tool, and touch three different systems in the time it takes a security analyst to read one alert. A command that looks permitted on paper can still be the wrong action, because what makes it risky is not the command itself but the intent behind it and how far the session has drifted from what the user actually asked for. That gap is what AI Detection and Response, or AIDR, exists to close: continuous monitoring of agent behavior at runtime, with the ability to intervene before a permitted-looking action turns into a real incident.

Choosing an AIDR platform in 2026 is harder than it should be, mostly because the category is still consolidating. Some vendors grew out of prompt-injection filtering and are extending toward agent behavior. Others started as identity or SaaS governance tools and added a runtime layer on top. A smaller group was built agent-first, with discovery, posture management, and detection designed around the reality that an agent's risk comes from its entire ecosystem: models, MCP servers, skills, plugins, and the identities it operates under, not just the prompt it receives.

Most AIDR platforms concentrate on the agent at the moment it acts. Dash Security takes a broader view, treating detection and response as one layer of a larger control plane that spans discovery, governance, posture management, and the agentic supply chain surrounding every agent it monitors.

The platform follows a four-stage model: discover, profile, harden, and enforce. Discovery, which Dash calls its Agentic FootPrint, maps known and shadow agents across workstations and managed cloud platforms, along with the models, MCP servers, skills, plugins, extensions, tools, identities, and connected data that expand what those agents can do. Runtime protection covers more than 20 coding agents by name, with discovery extending across more than 60 unique agent platforms, so the inventory is not limited to whichever tools a security team already knew about.

Detection is built on intent rather than commands alone. Dash's intent-similarity and intent-drift analysis compares what an agent was asked to do against what it is actually doing across a session, which is how it catches an agent that starts on a legitimate task and gradually moves away from it, something that looks identical to routine activity at the command level. At runtime, response actions scale with the severity of what is observed: inform a user, insert human-in-the-loop approval inside a live session, prevent an action outright, remediate an exposure, or export enriched context to the SOC.

Deployment is agentless and modular, running across Linux, macOS, and Windows with a single sensor, and the company markets discovery-to-enforcement in about a week. Dash's founding team has leadership experience from Palo Alto Networks, Akamai, and IBM, and the company is backed by YL Ventures, Wing, and Vesey Ventures, with a place in the CrowdStrike, AWS, and NVIDIA cybersecurity accelerator.

• Agentic FootPrint discovery across 60+ platforms and 20+ coding agents, including shadow AI
• Full agentic estate coverage spanning MCP servers, skills, plugins, models, identities, and connected data
• AI-SPM for continuous posture assessment and exposure remediation across the attack surface
• Intent similarity and intent-drift detection built on session context, not command inspection alone
• Human-in-the-loop enforcement that can pause a live session for approval
• AI DLP for sensitive-data exposure and unauthorized sharing at runtime
• Agentless, modular sensor deployment across Linux, macOS, and Windows
• AI Spend visibility across teams, platforms, models, and use cases

Zenity occupies a distinct position in the AIDR category, built around governance for agents and copilots embedded inside major SaaS ecosystems rather than agents running independently on workstations or in custom infrastructure. Its platform combines observability, AI Security Posture Management, governance, and runtime threat detection into one product.

The company's strength is coverage of AI built inside platforms like Microsoft Copilot Studio, Salesforce Agentforce, ServiceNow, Power Platform, AWS Bedrock, Azure AI Foundry, and Google Vertex AI, environments where business users and citizen developers, not just central engineering teams, are increasingly building agents. Zenity attributes agent ownership, applies policy before deployment, and correlates tool calls, memory access, and control flow at runtime to catch outcomes that look harmless at the input level. The trade-off is that depth is strongest inside those SaaS ecosystems; teams with a large footprint of locally run coding agents or custom-built infrastructure may need to evaluate coverage outside Zenity's core platforms.

• AI-SPM for configuration and permission risk across SaaS agent platforms
• Agent sharing and exposure analysis with ownership attribution
• Runtime monitoring of tool calls, memory access, and control flow
• Native support for Microsoft, Salesforce, ServiceNow, and major cloud AI services

Palo Alto Networks approaches AIDR through the lens of platform consolidation, folding agent security into a much larger cloud, network, and application security portfolio rather than treating it as a standalone product. Prisma AIRS covers AI posture management, model security, automated red teaming, AI runtime security, AI gateway controls, and agent security.

The platform's breadth is its main selling point. Organizations can assess AI assets before deployment, test agents and applications for weaknesses, inspect live traffic, and enforce controls in production, all from within the same vendor ecosystem that may already handle their network and cloud security. The trade-off is that this depth is strongest for organizations already invested in Palo Alto Networks infrastructure; teams standardized on other platforms may need to separately evaluate how much of their agent and MCP footprint falls outside that coverage.

• AI Gateway governance and inline control for AI traffic
• Agent identity and permission enforcement
• API-based and network-based runtime inspection
• Automated AI red teaming and model security scanning

Lasso Security specializes in intent-based detection for agentic workflows, built around what the company calls an Intent Security Framework that analyzes what an agent is trying to accomplish rather than relying purely on signature-style command rules.

The product spans CI/CD-stage discovery, posture management, automated red teaming backed by a large attack library, and runtime enforcement, with the company citing low-latency detection as a differentiator for production workloads. As an agent-native pure-play, Lasso's runtime engine is narrowly focused and can be a strong fit layered onto an existing security stack. The trade-off is scope: Lasso is more concentrated on detection and red teaming than on discovering and governing the full agentic supply chain, including MCP servers and third-party skills, so larger programs may pair it with separate identity or access tooling.

• Intent Security Framework for behavioral, not just command-level, analysis
• CI/CD-stage discovery ahead of production deployment
• Automated red teaming against a large attack library
• Low-latency runtime enforcement built for production traffic

Noma Security spans the AI lifecycle end to end, pairing runtime detection and response with agent access control, posture management, and adversarial testing under one platform. Its products share context with one another, so what discovery finds shapes access policy, and what access policy defines sharpens runtime detection.

Noma covers homegrown agents built on AWS Bedrock, Azure AI Foundry, and Databricks, SaaS agent platforms like Microsoft Copilot Studio and Salesforce Agentforce, and developer-facing agents such as coding assistants running on individual machines. Its runtime layer inspects prompts, responses, tool calls, and MCP server interactions, enforcing policy in real time through allow, mask, or block decisions. The trade-off relative to a dedicated estate-wide control plane is that Noma's public materials emphasize the AI application and model lifecycle somewhat more than deep governance of third-party MCP servers and skills as a distinct category of risk.

• Unified AI-DR, AI-SPM, Agent Access Control, and AI Red Teaming in one platform
• Cross-module context sharing between discovery, access control, and detection
• Coverage of major cloud AI platforms and developer coding agents
• Real-time policy enforcement with allow, mask, and block actions

WitnessAI approaches AIDR from the network layer, extending governance originally built for employee AI use to cover autonomous agents as well. The platform deploys inline at the network level, without endpoint agents or browser extensions, and organizes its capabilities into three modules: Observe, Protect, and Control.

Observe catalogs AI applications, agents, and MCP servers across the organization; Protect provides bidirectional runtime defense against prompt injection and data exfiltration; and Control enforces intent-based policies such as allow, warn, block, and route. WitnessAI supports single-tenant deployment for organizations with data sovereignty requirements and reports securing over 350,000 employees across 40-plus countries. The trade-off is that its agent and MCP governance capabilities are newer additions to a platform that originated around workforce AI use, so runtime depth on complex, multi-step agent sessions is still maturing relative to platforms built agent-first.

• Agentless, network-layer deployment with no endpoint footprint
• Observe, Protect, and Control modules covering discovery through enforcement
• Intent-based policy engine with allow, warn, block, and route actions
• MCP server and tool cataloging alongside employee AI visibility
• Single-tenant architecture available for data sovereignty requirements

Use this checklist to narrow the field before running a proof of concept:

• Map every agent surface first. Include coding agents in CLIs and IDEs, browser and desktop assistants, SaaS-native copilots, and any custom-built agents before evaluating a single vendor.
• Ask what "discovery" actually covers. Some platforms discover agents; fewer also discover the MCP servers, skills, and plugins those agents connect to.
• Test the response model, not just the alert. Confirm the platform supports graduated actions such as human-in-the-loop approval and partial remediation, not only block or allow.
• Time the path from discovery to enforcement. A platform that takes months to reach full coverage leaves agents unmonitored during the gap.
• Check whether pricing assumes you already run the vendor's other products. Platform-bundled options can be efficient for existing customers of that vendor and costly for everyone else.

AI Detection and Response is a security category focused on continuously monitoring AI agents and applications, identifying behavior that is unsafe, unauthorized, or inconsistent with a user's intent, and taking action during runtime. It typically includes agent discovery, session analysis, tool-use monitoring, data-loss prevention, and automated or human-approved response.

EDR and XDR correlate telemetry from endpoints, identities, and networks to catch known attack patterns. AIDR adds AI-native signals, prompts, tool calls, retrieved context, and session intent, that traditional telemetry does not capture. The two are complementary, and AIDR can enrich an XDR investigation with agent-specific context.

Leading platforms can detect and block many direct and indirect prompt-injection attempts, but effective protection usually combines several layers: input inspection, output controls, tool restrictions, and behavioral monitoring across the session. Because injected instructions can surface several steps into a task, filtering the initial prompt alone is rarely enough.

It depends on the platform. Some tools focus narrowly on an agent's prompts and outputs. Others, including Dash Security, extend discovery and governance across the MCP servers, skills, plugins, and models an agent connects to, treating the full agentic supply chain as part of the attack surface rather than a separate problem.

Timelines vary by architecture. Agentless, modular platforms tend to move fastest; Dash Security, for example, is built to go from discovery to enforcement in about a week. Platforms tied to an existing endpoint sensor or requiring per-application integration typically take longer to reach full coverage.

Any organization deploying AI agents with access to real systems or data has exposure, regardless of size. Smaller teams often start with discovery and posture management to understand their footprint before adding full runtime enforcement, scaling up as agent use grows.

AI Security Posture Management assesses risk before and between sessions, things like excessive permissions, exposed configurations, and orphaned agents, so teams can fix weaknesses proactively. AIDR operates during live sessions, watching behavior as it happens and intervening in real time. The two are complementary; posture management narrows what can go wrong, and AIDR catches what happens anyway.

Intent-based detection evaluates what an agent is trying to accomplish, not just the individual commands it issues. Platforms like Dash Security use intent similarity and intent-drift analysis to catch a session that has quietly moved away from its original purpose, something command-level telemetry alone typically cannot distinguish from routine activity.