AI agents are changing the unit of enterprise security. Traditional controls were designed around users, applications, workloads, and predictable execution paths. An AI agent is different: it interprets instructions, creates its own plan, selects tools, accesses data, and adjusts its actions as a task develops.
That flexibility creates a new detection challenge. A command may be technically permitted but inconsistent with the user’s objective. A file that looks like reference material may contain instructions that steer an agent. An authorized tool call may move sensitive information into the wrong system. An agent may begin with a legitimate task and gradually diverge from its intended purpose without triggering conventional malware or intrusion indicators.
The Top AIDR Platforms for 2026
1. Dash Security
Dash Security provides a control and intelligence layer for the agentic enterprise. Its platform is built around the idea that organizations need to secure not only individual prompts or models but the complete agentic estate: agents, users, sessions, models, MCP servers, skills, plugins, tools, identities, connected applications, data, and workflows.
The platform follows a four-stage model: discover, profile, harden, and enforce.
Discovery maps known and shadow agents operating across workstations, cloud platforms, and enterprise environments. It also identifies the ecosystem components that expand an agent’s capabilities or influence its behavior. This is significant because risk may originate in a skill, plugin, tool response, model, external source, or MCP server rather than in the agent’s core application.
Dash then builds profiles that connect each agent to its purpose, capabilities, users, behavior history, and session trajectory. Its intent-aware approach examines what the user wanted, what the agent attempted to accomplish, and whether the session moved away from that objective. This provides a different signal from command-level telemetry alone. Two agents may execute the same command, but the surrounding intent can make one action routine and the other a serious incident.
Hardening capabilities help security teams remediate weaknesses across the agentic attack surface and configure guardrails according to actor, platform, purpose, access, and risk. Instead of treating every agent identically, policies can reflect what a particular agent is intended to do and which resources it should reach.
At runtime, Dash monitors agent behavior for risks such as data leakage, unsafe commands, unauthorized actions, and intent drift. Response options can be proportionate to the situation. The platform can inform a user, introduce human approval, prevent an action, remediate an exposure, suspend activity, or export context to security operations.
Key capabilities include:
- Discovery of known and shadow AI agents
- Coverage across workstation and managed cloud environments
- Inventory of MCP servers, skills, plugins, and models
- Agent, user, purpose, and capability profiling
- Full AI session and event-trajectory analysis
- Intent similarity and intent-drift detection
- Agentic supply-chain risk assessment
- AI-specific runtime guardrails
2. Zenity
Zenity provides security and governance for enterprise AI agents across SaaS platforms, custom cloud environments, and end-user devices. Its platform combines observability, AI Security Posture Management, governance, runtime threat detection, and response.
The company’s approach reflects a shift from securing prompts to securing agent actions. Prompt inspection remains useful, but an autonomous agent may process a benign-looking request and still create a risky outcome through the tools, permissions, memory, and data available to it. Zenity therefore examines the agent’s broader architecture and execution path.
The platform supports agents embedded in major SaaS ecosystems, agents built using cloud AI services, and device-based tools. This multi-environment coverage is useful for enterprises whose AI programs span Microsoft Copilot Studio, Salesforce Agentforce, AWS Bedrock, Google Vertex AI, Microsoft Foundry, and internally developed agent frameworks.
Key capabilities include:
- AI-SPM for configuration and permission risks
- Agent sharing and exposure analysis
- Runtime monitoring of tool calls and data access
- Execution-path and control-flow analysis
3. Prompt Security
Prompt Security, now part of SentinelOne’s AI security portfolio, focuses on protecting AI use across employees, developers, custom applications, and autonomous agents. Its capabilities span AI usage discovery, data protection, prompt and response inspection, policy enforcement, and controls for agentic workflows.
One of its principal use cases is workforce AI governance. Employees can introduce sensitive information into public AI services through prompts, file uploads, browser interactions, or developer tools. Traditional web controls may identify the application being accessed without understanding the AI-specific content or deciding whether part of an interaction should be permitted.
Key capabilities include:
- Coverage for generative AI and coding assistants
- Acceptable-use policy enforcement
- Selective redaction of sensitive information
- Prompt and model-response inspection
4. Lakera Guard
Lakera Guard provides runtime protection for generative AI applications, models, agents, and MCP-enabled systems. Its foundation is real-time analysis of AI interactions, with controls designed to detect adversarial prompts, unsafe model behavior, sensitive-data exposure, and risky agent activity before those interactions produce harmful outcomes.
Lakera has built a substantial threat-intelligence foundation around prompt attacks and adversarial AI behavior. Its Gandalf environment has exposed the company to attack techniques contributed by a large global user community. That research informs protections against prompt injection, jailbreaks, malicious instructions, and evolving strategies used to manipulate AI systems.
Key capabilities include:
- Jailbreak and adversarial-instruction detection
- Input and model-output controls
- Sensitive-data leakage prevention
5. Palo Alto Networks Prisma AIRS
Palo Alto Networks Prisma AIRS is a broad AI security platform covering applications, agents, models, data, development pipelines, cloud environments, and production interactions. Its capabilities include AI posture management, AI model security, automated red teaming, AI runtime security, AI gateway controls, and agent security.
The platform’s breadth distinguishes it from products concentrated on a single layer. Organizations can assess AI assets before deployment, test applications and agents for weaknesses, inspect live traffic, and enforce security controls during production use.
Key capabilities include:
- AI Gateway governance and control
- Agent identity and permission enforcement
- API-based runtime inspection
- Network-based AI runtime protection
How to Choose the Right AIDR Platform
The right platform depends on where agents operate and what authority they possess. A generic product comparison cannot replace an architecture-specific evaluation.
Map the Complete Agentic Footprint
Begin with every place AI is used or built:
- Local coding agents
- Desktop assistants
- Browser-based AI tools
- Enterprise copilots
- SaaS-native agents
- Custom AI applications
- Cloud agent-building platforms
- MCP servers and clients
- AI skills, plugins, and extensions
- Autonomous business workflows
- Multi-agent systems
Security teams should then map identities, permissions, credentials, tools, data stores, and connected systems. An agent with read-only access to public documentation has a fundamentally different risk profile from one that can merge code, send email, modify cloud infrastructure, or update customer records.
Distinguish Interaction Security From Agent Behavior Security
Prompt and response inspection is valuable, especially for customer-facing AI applications. However, it does not automatically provide visibility into everything an autonomous agent does.
Ask whether the platform analyzes:
- Individual prompts or complete sessions
- Model outputs or execution trajectories
- Tool calls and their parameters
- Memory and retrieved context
- Effective agent permissions
- Cross-application actions
- The relationship between user intent and agent behavior
- Long-running or asynchronous tasks
- Actions performed by subagents
This distinction is central to selecting an AIDR platform rather than a narrower AI firewall.
Evaluate the Response Model
A binary allow-or-block decision may be too rigid for enterprise AI. Strong platforms support graduated actions according to confidence and impact.
An organization may want to:
- Allow a low-risk action
- Redact one sensitive field
- Warn and educate the user
- Request explicit approval
- Restrict a particular tool
- Prevent an external transmission
- Suspend the affected agent session
- Revoke access or remediate a configuration
- Escalate an enriched incident to the SOC
The response should occur early enough to prevent harm while preserving legitimate work.
Test Context Quality, Not Just Detection Volume
A long list of detections can produce more noise rather than more security. During evaluation, give analysts realistic scenarios and examine whether the platform explains:
- What happened
- Why it was considered risky
- What influenced the agent
- Which identity and permissions were involved
- Which data and systems were exposed
- Whether the agent departed from its intended purpose
- What action the platform took
- What the analyst should do next
High-quality context can reduce investigation time and support more precise automation.
FAQs
What is AIDR?
AI Detection and Response is a security category focused on continuously monitoring AI applications and agents, identifying dangerous or unintended behavior, and taking action during runtime. AIDR can include agent discovery, prompt-injection detection, session analysis, tool-use monitoring, data protection, behavioral analytics, policy enforcement, and automated response.
How is AIDR different from XDR?
XDR correlates security telemetry across endpoints, identities, networks, cloud services, email, and other enterprise systems. AIDR adds AI-native understanding, such as prompts, agent sessions, intent, model interactions, tool calls, retrieved context, and autonomous actions. The two technologies can work together, with AIDR providing agent context to broader XDR investigations.
Can AIDR prevent prompt injection?
Leading platforms can detect and block many direct and indirect prompt-injection attempts. Effective protection may involve input analysis, output controls, context inspection, tool restrictions, data policies, behavioral monitoring, and human approval. Because agentic attacks can span multiple steps, prompt filtering should be combined with monitoring of actions and execution paths.
Which AIDR platform is best for enterprise AI agents?
Dash Security is particularly strong for enterprises seeking agentic-estate discovery, deep session traceability, intent-aware detection, posture management, governance, and runtime response in a unified platform. Other products may align with SaaS-agent governance, workforce AI controls, inline prompt protection, or broader cloud and model-security programs.
