Runtime Monitoring & Threat Detection for Agentic AI: Top 8 Providers

• Agentic AI Runtime Security Providers at a Glance
• Why Agentic AI Requires Runtime Threat Detection
• Key Threats Runtime Monitoring Helps Detect

• Key Capabilities to Look for in Agentic AI Runtime Security Providers
• Notable Agentic AI Runtime Monitoring and Threat Detection Providers

Agents Can Take Autonomous Actions

Agentic AI systems can perform actions without waiting for a human to approve every individual step. Depending on their permissions, agents may execute code, update databases, send messages, make API requests, create files, or initiate transactions. This autonomy increases the impact of incorrect or manipulated decisions because an unsafe output can immediately become a real action.

Runtime threat detection provides a control point between the agent’s decision and the systems it can affect. It can detect prohibited operations, unusual sequences of actions, attempts to exceed assigned permissions, or repeated actions that indicate the agent is stuck in an unsafe loop. High-risk actions can then be blocked, terminated, or sent for human approval.

Agents Interact with Sensitive Data and Systems

Agents often need access to internal applications, customer information, databases, credentials, and proprietary documents to complete useful tasks. An agent with broad access may retrieve sensitive information even when only a small subset is required. It may also expose that information through model outputs, logs, tool calls, or external services.

Runtime detection can evaluate data access in the context of the agent’s current task. For example, it can flag requests for unrelated records, unusually large data retrievals, attempts to access restricted resources, or transfers of sensitive data to unauthorized destinations. This adds protection when authentication and static access controls alone cannot determine whether an otherwise permitted action is appropriate.

Agent behavior is not determined only by its initial prompt. During execution, an agent may retrieve documents, receive tool responses, use stored context, generate intermediate plans, and adapt its next action based on new information. This means a run that begins with a legitimate request can move into an unsafe state later.

Runtime monitoring evaluates the execution continuously rather than relying only on checks performed before the agent starts. It can detect unexpected changes in goals, repeated failures, suspicious tool-call chains, privilege escalation attempts, or actions that diverge from defined policies. Monitoring the full trajectory is especially important for long-running agents, where many individually valid actions can combine into an unsafe outcome.

1. Prompt Injection and Indirect Prompt Injection

Prompt injection occurs when untrusted instructions influence an agent to ignore or work around its intended rules. Direct injection can come from a user, while indirect prompt injection can be embedded in:

• Web pages
• Emails
• Documents
• Tool responses
• Retrieved data

Runtime monitoring can inspect the agent’s behavior after it encounters untrusted content. It can flag unexpected changes in execution, attempts to reveal protected information, suspicious tool calls, or actions that conflict with system policies. This is important because detecting malicious text alone does not guarantee that the agent will not act on it.

2. Malicious or Unauthorized Tool Calls

Agents can use tools to:

• Execute code
• Query databases
• Modify files
• Send messages
• Interact with external APIs

A compromised or misdirected agent may call a legitimate tool for an unauthorized purpose or supply dangerous parameters to an otherwise permitted operation. Runtime controls can validate the tool, operation, arguments, destination, and execution context before allowing a call. They can block prohibited operations, enforce parameter constraints, require approval for high-risk actions, and detect unusual sequences of tool use.

3. Excessive Agent Permissions

Agents may be given broader permissions than they need, particularly when a single identity or API credential is reused across multiple tasks. Excessive permissions increase the potential impact of:

• Prompt injection
• Configuration errors
• Compromised agent behavior

Runtime monitoring can detect when an agent attempts to use capabilities that are unnecessary for its current task. Combined with least-privilege access controls, monitoring can identify unusual resource access and provide evidence for reducing permissions that are rarely or incorrectly used.

4. Sensitive Data Exposure and Exfiltration

Agents can encounter credentials, personal information, proprietary documents, source code, and other sensitive data while completing tasks. That information may be exposed through:

• Model responses
• Tool arguments
• External requests
• Logs
• Generated files

Runtime monitoring can inspect data flows across model and tool boundaries. It can detect sensitive values, unusual volumes of retrieved data, unauthorized destinations, or attempts to encode and transfer protected information. Depending on policy, the system can redact the data, block the transfer, or require additional authorization.

5. Agent Hijacking

Agent hijacking occurs when an attacker causes an agent to pursue instructions or objectives that differ from its intended task. Prompt injection is one possible mechanism, but the following can also redirect an agent:

• Manipulated tool responses
• Compromised external content
• Poisoned context

Runtime detection can compare actions against the original task, permitted workflow, and expected resource usage. Large deviations, unexpected external communications, or new objectives introduced by untrusted content can trigger additional validation or stop execution.

6. Privilege Escalation

Privilege escalation occurs when an agent attempts to gain or exercise access beyond the privileges assigned to it. This can include:

• Requesting elevated credentials
• Accessing restricted resources through another tool
• Changing security settings
• Exploiting a more privileged service

Runtime monitoring can detect these attempts across a sequence of actions, including cases where each individual operation appears harmless. Controls can enforce identity boundaries, prevent unauthorized credential use, and require explicit approval before an agent performs privileged operations.

Attackers can abuse models and connected tools for purposes outside the intended use of an agent. Examples include:

• Automated scraping
• Spam generation
• Resource exhaustion

• Unauthorized code execution
• Repeated probing of internal systems
• Using tools as proxies to reach restricted resources

Runtime monitoring can identify abuse through request frequency, token and compute consumption, tool-call patterns, destinations, and repeated failures. Rate limits, execution quotas, anomaly detection, and policy checks can then restrict abusive activity while preserving normal agent workflows.

Real-Time Agent Activity Monitoring

A runtime security platform should provide continuous visibility into what agents are doing while they execute. This includes model requests, tool calls, API activity, state changes, data access, and actions taken in external systems.

Monitoring should preserve enough context to reconstruct an agent’s execution path rather than recording isolated events. Security teams can then trace an incident from the original request through intermediate decisions and tool interactions to the final action. The platform should also support real-time blocking or approval workflows when it detects high-risk behavior.

Prompt and Response Inspection

The platform should inspect prompts and model responses for threats such as prompt injection, malicious instructions, sensitive data disclosure, and policy violations. Inspection should cover more than direct user prompts because agents receive instructions and data from many sources.

This includes retrieved documents, web content, emails, memory, and tool responses that become part of the model context. Effective controls should distinguish trusted instructions from untrusted content and apply policies before dangerous outputs are passed to tools or external systems.

Tool Call and Function Monitoring

Tool calls are a critical enforcement point because they convert model decisions into actions. Runtime security should capture the selected tool, function, arguments, destination, execution result, and the context that caused the agent to make the call.

Providers should support policies that restrict specified tools, operations, parameters, and destinations. Useful controls include argument validation, allowlists, rate limits, transaction thresholds, and human approval for sensitive operations. Monitoring should also identify suspicious chains of individually permitted tool calls.

Agent Identity and Access Monitoring

Security controls need to identify which agent is acting, which user or service initiated the task, and what permissions apply to the execution. Without this identity context, it is difficult to determine whether a technically valid action is authorized.

Providers should integrate agent activity with authentication and authorization controls and record the identities involved in each action. Monitoring can then detect access to unexpected resources, use of inappropriate credentials, or actions that exceed the permissions required for the current task.

Behavioral Anomaly Detection

Static rules cannot describe every unsafe sequence an autonomous agent might produce. Behavioral monitoring can identify deviations from normal or expected activity, such as unusual tool sequences, sudden increases in data access, repeated failures, or unexpected external connections.

Anomaly detection should use execution context rather than treating every unusual event as a threat. Providers should make detections explainable enough for security teams to understand which behavior triggered an alert and determine whether policies or agent logic need adjustment.

Data Loss Prevention for AI Workflows

Agentic workflows can move sensitive information between models, databases, tools, files, and external services. Runtime security should inspect these data flows for credentials, personal data, proprietary information, and other protected content.

Data loss prevention controls should be able to redact, tokenize, block, or restrict sensitive information based on its destination and purpose. Policies should apply consistently across prompts, responses, tool arguments, retrieved content, and agent-generated outputs rather than protecting only the model interface.

MCP Server and Tool Security

Model Context Protocol (MCP) servers can expose tools and resources to agents through a standardized interface. Runtime security should therefore provide visibility into which MCP servers an agent connects to, which tools they expose, and how those tools are used during execution.

Useful controls include server allowlists, tool-level permissions, argument validation, authentication monitoring, and detection of unexpected changes in available capabilities. Providers should also monitor responses from MCP servers because compromised or untrusted tool output can introduce malicious instructions or data into an agent’s context.

Multi-Agent Interaction Monitoring

Multi-agent systems introduce communication paths that do not exist in single-agent applications. One agent may delegate tasks, share context, provide results to another agent, or invoke another agent with different permissions. A compromised agent can therefore influence other components of the system.

Runtime security should trace messages, delegated tasks, data transfers, and actions across agent boundaries. It should preserve the identities and permissions of participating agents so teams can determine where an unsafe instruction originated and how it propagated through the system. Controls should also prevent one agent from using another agent to bypass its own access restrictions.

Best for: SaaS, homegrown and developer-hosted AI agent estates

Strengths: Intent-based runtime monitoring, agent mapping, MCP tool control

Things to consider: Recent product, so independent review coverage is thin

Radware Agentic AI Protection monitors and blocks agent-targeted manipulation and attacks while agents run. It identifies intent automatically and responds in real time, rather than relying only on checks performed before execution.

The product maps the agent environment, including agents, the tools they use and the interactions between them. It applies guardrails to prompts and to tool calls, blocks actions that diverge from an agent's goal, and scores risk continuously across agents and tools.

Coverage extends across SaaS platforms, agents built in house and agents running on developer endpoints, including Claude Code. Independent testing rated its blocking of indirect prompt injection attacks at up to 95.7% accuracy.

Key features include:

Limitations (based on publicly available sources):

Source: Radware

Best for: Teams standardizing AI security on the Palo Alto platform

Strengths: Runtime enforcement, agent identity checks and AI red teaming

Things to consider: Some capabilities are newer and need tuning per environment

Prisma AIRS covers discovery, assessment and protection for AI agents, applications, models and data from development through deployment. Its runtime component monitors AI behavior and enforces safeguards during live interactions.

The platform groups several modules under one console. Agent Security verifies agent identity and enforces controls on agent actions, while AI Posture Management tracks the data used for training and inference, the integrity of agents and applications, and access to deployed models.

Deployment uses Network Intercept and API Intercept modes, and a managed runtime security option is also available. The Precision AI engine inspects AI traffic for prompt injection, sensitive data exposure, malicious URLs and toxic content.

Key features include:

Limitations (as reported by users on Gartner Peer Insights):

Source: Palo Alto Networks

Best for: Securing agents in Microsoft, Salesforce and low-code platforms

Strengths: Decision-layer enforcement, exposure validation, agent inventory

Things to consider: Coverage is strongest in Microsoft-centric agent estates

Zenity is built around the point at which an agent makes a decision. The platform is organized into three layers: Surface, which gathers what a decision depends on, Enforce, which acts as the decision is made, and Protect, which monitors what happens afterwards.

Runtime Boundaries evaluate each AI action against the agent's broader execution history, including intent, identity, requested action, accessed data, tools and prior activity, so risks that build across several steps can be stopped before the action completes.

The platform spans agentic SaaS tools, cloud and homegrown agents, and agents running on end-user devices. AIDR monitors agent execution step by step and maps detections to OWASP and MITRE ATLAS.

Key features include:

Limitations (based on publicly available sources):

Best for: Homegrown, SaaS and endpoint agents needing session-level detection

Strengths: Session-level behavior analysis, tunable detectors, MCP governance

Things to consider: Enforcement mechanics and pricing are not published

Noma AI-DR monitors the full chain of agent actions rather than individual prompts. The stated reasoning is that risk in agentic systems usually appears as a sequence of individually legitimate actions, such as reading customer records early in a session and emailing a summary externally several steps later.

The Runtime Context Engine inspects agent interactions across five layers: event, session, identity, data and baseline. It combines that with posture context from AI-SPM, including identity and blast radius, and baselines behavior over time.

Detectors can be tuned individually for sensitivity and for the action taken, and AI Guardrails allow custom detection logic to be written in natural language.

Key features include:

Limitations (based on publicly available sources):

Best for: Model security combined with agentic workflow protection

Strengths: Agentic runtime visibility, inline enforcement, SIEM integration

Things to consider: Depth of agent action tracing should be verified

HiddenLayer AI Runtime Security protects AI applications, coding agents and agentic workflows while they operate. It provides runtime visibility, threat detection and inline enforcement across AI endpoints, applications and agent workflows.

The module addresses three problem areas: prompt injection and indirect attacks arriving through prompts, retrieved content or tool output; sensitive data exposure of source code, credentials and proprietary information; and unauthorized agent actions against applications, data and systems.

It sits alongside AI Discovery, AI Supply Chain Security and AI Attack Simulation within the wider platform, which can be deployed as SaaS, on premises, air gapped or hybrid.

Key features include:

Limitations (based on publicly available sources):

Source: HiddenLayer

Best for: Production AI apps needing model-specific runtime guardrails

Strengths: Automated guardrail configuration and network-level enforcement

Things to consider: Documentation can be hard to navigate during configuration

Cisco AI Defense AI Runtime Protection defends production AI applications from attacks and unwanted responses. Guardrails are configured automatically against the vulnerabilities identified for each model through AI Model and Application Validation.

On the input side it inspects every request and blocks malicious payloads before they reach the model, covering prompt injection, prompt extraction, denial of service and command execution, and stopping personally identifiable information from reaching the model.

On the output side it scans responses for sensitive information, hallucinations and harmful content, including data drawn from fine-tuning or from databases connected for retrieval-augmented generation.

Key features include:

Limitations (as reported by users on Gartner Peer Insights):

Source: Cisco

Best for: Runtime screening of prompts, outputs and agent tool use

Strengths: Agent and MCP discovery, real-time blocking, low added latency

Things to consider: Default strict threshold can raise false positives

Lakera AI Agent Security enforces controls on every AI interaction by inspecting inputs, controlling outputs and governing what agents do. It deploys without changes to models or prompts and adds minimal latency.

Before enforcement, it provides visibility into agent usage and MCP-connected systems across the environment, including agents that teams did not explicitly build or register, and identifies the data, APIs and systems each agent can reach.

At runtime it blocks prompt attacks, data leakage, unsafe tool use and unauthorized agent actions. It also blocks indirect injection arriving through connected tools before agents act on compromised instructions.

Key features include:

Limitations (based on publicly available sources):

Source: Lakera

Best for: Governing employee and agent AI use under one policy layer

Strengths: Agent and MCP inventory, intent-based controls, audit trails

Things to consider: Cost and tuning effort grow with data volume

WitnessAI covers human and agent AI use through a single security and governance platform. It is organized into three functions: Observe for visibility, Control for governance, and Protect for runtime defense.

Observation extends to agent activity and to the external MCP servers and tools agents connect to, with prompts, responses and agentic activity visualized in real time and classified by type and intent.

Protection applies runtime security controls and content guardrails across models, applications and agents. Intent-based machine learning engines are used to evaluate context rather than matching keywords.

Key features include:

Limitations (as reported by users on Gartner Peer Insights):

Source: WitnessAI