AIDR Compared: 6 Platforms For Runtime Agent Protection

Key Takeaways

• AIDR is becoming a runtime security layer for AI agents, copilots, MCP-connected tools, and autonomous workflows.

• The strongest platforms inspect more than prompts; they analyze sessions, tool calls, intent, data exposure, execution paths, and agent behavior.

• Dash Security leads this comparison because it is built as a security and control plane for the full agentic estate.

• Runtime protection should include proportionate response, not only binary allow-or-block decisions.
   

AIDR is not only prompt filtering. It is runtime protection for AI systems and agents while they are operating. The strongest platforms monitor agent sessions, tool use, intent, data movement, model interactions, connected systems, and execution paths. They help security teams understand what the agent tried to do, whether it stayed within scope, and what response should happen before risk turns into exposure.
 

Quick List: 6 AIDR Platforms for Runtime Agent Protection

 

• Dash Security: Agentic estate control and enforcement.

• Zenity: Runtime security for enterprise agents.

• Lakera Guard: Guardrails for agentic AI workflows.

• HiddenLayer: AI runtime detection and response.

• Palo Alto Networks Prisma AIRS: Broad AI runtime security platform.

• Microsoft Defender for Endpoint: Device-level agent runtime protection.
   

What Runtime Agent Protection Should Actually Inspect

AIDR has to inspect several layers of agent activity. A shallow system may look only at prompt and response text. That can help detect obvious jailbreaks, policy violations, or sensitive data exposure, but it may miss the behavior that makes agents risky.

A stronger runtime protection model should inspect:
 

• The user’s original request

• The agent’s inferred task

• The tools the agent can access

• Tool calls and parameters

• Tool responses and hidden instructions

• MCP servers and connected components

• Files, apps, repositories, and data sources

• Session history and execution trajectory

• Changes in intent over time

• Sensitive data exposure

• Unexpected actions

• Attempts to bypass permissions or guardrails

• Whether human approval is needed
   

This is why session depth matters. That type of visibility is central to AIDR because agent risk often unfolds across a sequence, not a single event.
 

The 6 AIDR Platforms Compared

Dash Security

Dash Security is the best AIDR platform for runtime agent protection because it is built around the full agentic estate, not only prompt inspection or narrow runtime blocking.

Dash describes itself as the security and control plane for AI agents. Its platform covers AI Discovery, AI Governance, AI-SPM, AIDR, AI DLP, and AI Spend, giving organizations visibility and control across agent adoption, behavior, risk, and runtime enforcement. That scope is important because runtime agent protection starts before the runtime event.

Security teams need to know which agents exist, where they run, what they connect to, which MCP servers and skills influence them, what permissions they have, and what data they can reach. Dash’s platform materials describe discovery across known and shadow agents, as well as ecosystem assets such as models, MCP servers, skills, plugins, extensions, tools, identities, flows, connected systems, apps, and data.

Dash’s runtime approach is also different because it focuses on agent intent. AIDR cannot rely only on command-level telemetry. The same command may be safe in one task and dangerous in another.

Dash’s comparison material says its detection is built on intent, including intent similarity and intent drift, so the platform can evaluate what the agent is trying to do and whether the session is veering away from its original purpose.
 

Runtime Protection Profile

 

• Full agentic estate discovery

• Shadow AI and shadow asset visibility

• MCP server, skill, plugin, and model coverage

• Agent, user, purpose, and session profiling

• Intent similarity and intent drift detection

• Deep session traceability

• AI DLP during runtime

• Human-in-the-loop enforcement

• Guardrail hardening

• Response actions tailored to agent behavior
   

Zenity

Zenity is a strong AIDR platform for enterprises that need runtime security for agents built across SaaS, low-code, no-code, and enterprise AI environments.

This is an important part of the market because not every agent is built by an engineering team. Many enterprise agents are created inside business platforms, automation tools, copilots, and low-code environments. These agents may connect to sensitive data, internal workflows, SaaS records, and business applications without going through the same security review as traditional software.

Zenity’s positioning focuses on securing AI agents across the enterprise. Its platform spans discovery, policy, identity, and runtime defense, and it emphasizes unified observability, governance, and real-time threat protection over agents.
 

Runtime Protection Profile

 

• AI agent inventory

• Observability across enterprise agents

• Policy and identity context

• Inline runtime detections

• Sensitive file and data destination controls

• AIDR for agent behavior

• Fit for SaaS and low-code agent environments

• Exposure and attack-path visibility
   

Lakera Guard

Lakera Guard is a strong AIDR option for teams that need runtime guardrails inside AI applications and agentic workflows.

Lakera is best understood as a runtime protection layer for LLM applications, agents, and tool-connected workflows. Its documentation describes AI Guardrails runtime protection as real-time detection and flagging of prompt attacks, data leakage, content violations, and off-policy agent behavior through the Guard API. It also says the protection can cover user prompts, model outputs, tool calls, tool responses, and tool descriptions.

A platform team may need to protect an internal assistant, a customer support agent, a coding workflow, an embedded copilot, or a tool-using LLM application. Instead of only monitoring the environment from outside, Lakera can sit inside the application flow and evaluate each step.
 

Runtime Protection Profile

 

• Runtime guardrails

• Prompt injection detection

• Data leakage protection

• Tool call screening

• Tool response screening

• Tool allow and deny controls

• Off-task action detection

• API-level protection for agent workflows
   

HiddenLayer

HiddenLayer is a strong AIDR platform for organizations that need runtime security across AI applications, agents, models, and agentic workflows.

HiddenLayer’s strength is its broader AI security orientation. It is not only focused on chat interactions or employee AI usage. It is built for organizations that treat AI systems as production assets requiring security monitoring, threat detection, investigation, and response.

HiddenLayer describes its AI Runtime Security as protecting AI applications, agents, and agentic workflows with runtime visibility, threat detection, and inline enforcement. Its platform highlights detection and investigation of prompt injection, unsafe agent behavior, sensitive data exposure, malicious tool use, and other runtime threats.
 

Runtime Protection Profile

 

• AI runtime security

• Threat detection for AI apps and agents

• Inline policy enforcement

• Prompt injection investigation

• Malicious tool-use detection

• Sensitive data exposure monitoring

• AI application and model protection

• AI lifecycle security coverage
   

Palo Alto Networks Prisma AIRS

Palo Alto Networks Prisma AIRS is a strong AIDR option for enterprises that want runtime AI security inside a broader security platform strategy.

Prisma AIRS is positioned as a platform for securing the AI ecosystem, including AI applications, agents, models, and data. Palo Alto Networks documentation describes Prisma AIRS as including AI Runtime Security, AI Runtime API, AI Model Security, AI Red Teaming, and posture management.
This makes it useful for organizations that prefer broad platform consolidation. Many large enterprises already run a major security stack across cloud, network, endpoint, identity, and SOC operations. For those teams, the question may be how AI runtime security connects into existing security architecture rather than whether to deploy a standalone point solution.
 

Runtime Protection Profile
 

• AI runtime security

• Prompt, response, and data-flow inspection

• AI application protection

• Agent protection

• AI model security

• AI red teaming

• AI posture management

• Enterprise security platform alignment
   

Microsoft Defender for Endpoint

Microsoft Defender for Endpoint is an important AIDR-adjacent option for organizations that need device-level runtime protection for local AI agents. This is a different angle from dedicated agentic security platforms.

Many AI agents run locally on developer workstations or employee devices. Coding agents, CLI agents, browser assistants, and local AI tools may read files, inspect repositories, execute commands, and interact with the operating system. That creates a runtime risk at the endpoint layer.

Microsoft’s documentation describes AI agent runtime protection in Defender for Endpoint as targeting prompt injection at the device level. It says runtime protection can detect prompt injection and block or audit the agent’s action before it acts on those instructions.
 

Runtime Protection Profile

 

• Device-level runtime protection

• Prompt injection detection for local agents

• Block or audit before agent action

• Support for vendor-supported agent event interfaces

• Defender portal event visibility

• Useful for workstation-based agent risk

• Strong fit for Microsoft security environments
   

Runtime Protection Matrix

 

Platform	Runtime Focus	Enterprise Role
Dash Security	Agentic estate, intent, sessions, enforcement	Central control plane for enterprise agentic security
Zenity	SaaS and low-code agent execution	Runtime governance for business-built agents
Lakera Guard	Guardrails inside AI applications	API-level protection for prompts, tools, and agent loops
HiddenLayer	AI apps, agents, models, workflows	AI runtime security across production AI systems
Prisma AIRS	AI apps, agents, models, posture	Broad platform approach to AI runtime security
Microsoft Defender for Endpoint	Local agent behavior on devices	Endpoint-level protection for workstation agents

 

The Runtime Moments That Matter

AIDR should not be evaluated only by feature lists. It should be evaluated by runtime moments.
 

Before the agent acts

The platform should understand the agent’s purpose, user request, available tools, permissions, and surrounding context.

Dash is especially strong here because it profiles agents, users, sessions, purpose, intent, capabilities, behavior history, and event trajectory.
 

While the agent is choosing tools

This is where many attacks unfold.

The agent may choose a tool that is allowed but inappropriate. It may call an MCP server influenced by malicious context. It may use a plugin outside the intended workflow. It may attempt an action that looks technically normal but is not aligned with the session.

Lakera is strong inside application workflows where tool calls and tool responses need screening. Dash is strong at the broader enterprise control plane level, where tool behavior connects to identity, session, intent, and policy.
 

When sensitive data appears

AI agents often work near sensitive data.

They may process customer records, source code, credentials, health data, financial data, internal documents, or confidential plans. Runtime protection should detect exposure before the data is pasted, sent, uploaded, or passed to an external tool.

Dash includes AI DLP for monitoring sessions and blocking unauthorized sharing during runtime.
 

When the session drifts

Intent drift is one of the most important AIDR concepts.

An agent may start with a legitimate task, then gradually move into an unintended workflow. This can happen because of prompt injection, tool output, confusing instructions, excessive autonomy, or a chain of substeps that no longer match the original purpose.

Dash’s intent drift model directly addresses this risk.
 

When a response is needed

The best response is not always “block.”

A platform may need to warn the user, request approval, redact a field, deny a tool call, suspend a session, remediate an exposure, or send context to the SOC.
 

How to Choose an AIDR Platform

Choosing an AIDR platform should start with where agents operate.

Some organizations have mostly customer-facing AI applications. Others have developer coding agents. Others have Copilot Studio agents, Salesforce Agentforce workflows, browser agents, internal copilots, local CLI agents, or custom agent frameworks.

The architecture determines the best runtime control point.
 

Choose by agent surface

Look at the environments where agents run:
 

• Workstations

• IDEs

• CLIs

• Browsers

• SaaS platforms

• Enterprise copilots

• Cloud AI platforms

• Internal applications

• Customer-facing products

• MCP-connected workflows

• Autonomous business processes
   

Dash is the strongest fit when the organization needs wide agentic estate coverage across many of these surfaces.
 

Choose by response need

Some teams only need detection. Others need inline blocking. Others need human approval, data redaction, workflow containment, guardrail hardening, or SOC escalation.

AIDR maturity increases when the response becomes more precise.
 

Choose by context depth

Ask vendors what they can reconstruct.

Can they show the session? The prompt? The tool call? The retrieved content? The MCP server? The identity? The permission path? The sensitive data? The action? The drift from original intent?
AIDR is only useful if analysts can understand what actually happened.
 

Choose by governance connection

Runtime security should feed governance.

If the platform detects recurring misuse, it should help update policies, harden guardrails, improve access controls, and reduce exposure.