How to Choose Agentic AI Security Solutions: 8 Solutions Compared

1. Agent and Shadow Agent Discovery and Mapping: Find every agent, including unsanctioned ones, and map the tools, data, and systems each one reaches.
2. Integration Across SaaS, Coding, Developer-Hosted, and Home-Grown Agents: Cover commercial agent platforms, coding assistants on developer machines, and custom-built agents.
3. AI Security Posture Management (AI-SPM): Assess agent configuration, permissions, and risk before deployment and continuously afterward.

4. Compliance Reporting and Regulatory Alignment: Produce audit-ready records and map agent risk to recognized frameworks and regulations.
5. Intent-Based, Real-Time Runtime Protection: Judge what an agent is trying to do during execution and block manipulation such as prompt injection and tool misuse.
6. API Discovery and API Abuse Protection: Discover the APIs agents call and prevent excessive, unauthorized, or automated abuse of them.
7. Support for MCP and Agent Tool Ecosystems: Discover MCP servers and tools, and control which tools each agent can invoke.

Runtime Protection and Posture Management

• Radware Agentic AI Protection: Intent-based runtime protection, agent and tool discovery, MCP tool control, posture management with audit-ready reporting.
• Palo Alto Networks Prisma AIRS: Agent discovery, supply chain and MCP scanning, red teaming, runtime protection, and posture management across the AI lifecycle.
• Zenity: Agent observability, AI-SPM, exposure validation, MCP gateway controls, and step-level runtime detection.
• Prompt Security (SentinelOne): Shadow AI and shadow MCP discovery, real-time AI firewall, least-privilege agent scope, searchable audit logging.

Agentic Identity and Access Governance

• Idira Secure AI Agents (formerly CyberArk): Agent discovery with ownership context, task-scoped privileges through an AI Agent Gateway, and audit trails.
• Astrix Security: Inventory of agents, MCP servers, and non-human identities, Identity Graph mapping, and secure-by-design agent provisioning.
• Oasis Security Agentic Access Management: Intent analysis, ephemeral per-session identities, AI-SPM checks, and full chain-of-custody audit trails.
• SailPoint Agent Identity Security: Agent aggregation from cloud and agent platforms, ownership assignment, access reviews, and service account governance.

• Why Choosing the Right Agentic AI Security Solution Matters
• How to Choose Agentic AI Security Solutions
• Common Agentic AI Security Solutions and How They Meet the Criteria

• Notable Agentic AI Security Solutions and How They Meet the Criteria
  1. Runtime Protection and Posture Management
  2. Agentic Identity and Access Governance

• Agents increase the blast radius of AI risk: Autonomous agents can interact with multiple systems, APIs, and data sources. If compromised or overprivileged, they can spread attacks across connected environments, making rapid detection, containment, and least-privilege enforcement essential.
• Governance is lagging behind adoption: Many organizations deploy AI agents before establishing governance policies, audit processes, and accountability. Security solutions should enforce policies, maintain detailed audit trails, and support regulatory compliance throughout the agent lifecycle.

• Shadow AI and unapproved agents add more risk: Employees can deploy AI agents outside approved processes, creating security blind spots. Effective solutions should discover unauthorized agents, monitor their activity, and apply consistent security controls across both approved and shadow deployments.

1. Agent and Shadow Agent Discovery and Mapping

Discovery is the starting point for every other control. Organizations typically cannot list the agents running in their environment, because business users create agents in SaaS platforms, developers spin them up locally, and teams build custom agents that never pass through review. A solution has to find those agents without relying on a registry, and then map what each one connects to, so that permissions and behavior can be judged in context rather than agent by agent in isolation.

Evaluation criteria:

• Automatic discovery: Find agents across SaaS platforms, cloud services, low-code tools, custom builds, and end-user devices without manual registration.
• Shadow agent detection: Surface agents created outside approved processes, including those hidden behind tokens, service accounts, and ephemeral sessions.
• Relationship mapping: Show which tools, data sources, systems, and other agents each agent can reach, and where dependencies exist.
• Ownership and metadata: Attach an owner, purpose, configuration, and permission set to every discovered agent.
• Continuous refresh: Update the inventory as agents are created, modified, and retired, rather than producing a point-in-time snapshot.

2. Integration Across SaaS, Coding, Developer-Hosted, and Home-Grown Agents

Most organizations end up with agents in at least three shapes: agents embedded in or built on commercial SaaS platforms, coding agents running on developer machines, and home-grown agents built on cloud AI services or open frameworks. These live in different places and are instrumented differently, so a solution strong in one shape can be blind in another. Coverage breadth is worth checking against the specific mix an organization already runs.

Evaluation criteria:

• Agentic SaaS coverage: Support platforms such as Microsoft Copilot Studio, Salesforce Agentforce, ServiceNow, and enterprise ChatGPT or Claude deployments.
• Developer-hosted and coding agent coverage: Monitor coding agents such as Claude Code, GitHub Copilot, and Cursor on client-side endpoints.
• Home-grown agent coverage: Work with agents built on AWS Bedrock, Google Vertex AI, Microsoft Foundry, and custom orchestration code.
• Deployment model: Confirm what the solution requires to integrate, whether an agent, a gateway, API-based connections, or code changes.
• Consistent policy across shapes: Apply the same policies to all three shapes rather than maintaining separate rule sets per environment.

Related content: Read our guide to LLM security for protecting the models behind your agents.

3. AI Security Posture Management (AI-SPM)

AI-SPM is the preventive half of agentic AI security. It examines how an agent is built and permissioned, rather than what it is doing right now: which instructions it carries, what memory and data it can read, which tools it is wired to, and how far its permissions exceed its job. Catching an over-permissioned agent before it goes live is cheaper than detecting the resulting incident, so posture depth and the ability to gate deployment both matter.

Evaluation criteria:

• Configuration assessment: Evaluate agent instructions, memory access, data connections, and tool permissions against policy.
• Over-permission detection: Identify excessive privileges and standing access, and support revoking them.
• Risk scoring and prioritization: Rank findings by exploitability and business impact so teams can work the list in order.
• Pre-deployment gating: Apply posture policy before an agent reaches production, not only after.
• Remediation workflow: Route findings to the right owner and integrate with existing ITSM, SIEM, and SOAR tooling.

4. Compliance Reporting and Regulatory Alignment

Agentic AI is landing in the middle of a widening set of AI-specific obligations, including ISO 42001, the EU AI Act, and the NIST AI Risk Management Framework, alongside existing regimes such as GDPR, HIPAA, PCI DSS, and SOX. Auditors ask what agents exist, who owns them, what they accessed, and what was blocked. Solutions differ sharply here: some generate audit-ready reports mapped to named frameworks, while others produce raw logs that a team has to assemble into evidence.

Evaluation criteria:

• Audit-ready reporting: Generate reports that can be handed to an auditor without manual reconstruction.
• Framework mapping: Map agent risks and controls to named standards and frameworks rather than to generic risk categories.
• Complete activity records: Log prompts, intents, tool invocations, policy decisions, and outcomes with enough detail to reconstruct a session.
• Ownership and accountability records: Maintain documented agent ownership, including succession when roles change.
• Log integrity and export: Protect records against modification and export them to SIEM and analytics platforms.

5. Intent-Based, Real-Time Runtime Protection

Posture and identity controls do not stop an agent that has been manipulated into misusing access it legitimately holds. Runtime protection addresses that case by evaluating what an agent is attempting during execution. Intent-based approaches assess the agent's goal across a chain of steps rather than scanning individual prompts, which is what catches indirect prompt injection, jailbreaks, and tool misuse that look benign one request at a time.

Evaluation criteria:

• Intent evaluation: Judge agent behavior across multi-step and cross-agent execution paths, not just single prompts and responses.
• Manipulation defenses: Detect and block indirect prompt injection, jailbreaks, memory manipulation, and supply chain attacks.
• Enforcement mode: Confirm whether the solution can block inline, or only observe and alert out of band.
• Guardrails on model interactions: Validate prompts and responses to prevent unsafe outputs and data leakage.
• Automated response: Suspend, quarantine, or restrict an agent automatically when it exceeds its role.

Related content: Learn how an AI firewall inspects and filters AI-driven traffic.

6. API Discovery and API Abuse Protection

Agents do their work through APIs. That makes the API layer both the place where agent activity is most visible and the place where a compromised agent does the most damage, at machine speed and volume. Many agent security solutions govern the identity that calls an API without seeing the API traffic itself, so it is worth separating solutions that discover and protect APIs from those that only control access to them.

Evaluation criteria:

• API discovery: Identify documented, undocumented, and shadow APIs that agents reach across cloud and on-premises environments.
• Agent-to-API visibility: Map which agent called which API, what data it accessed, and which permissions it exercised.
• Abuse prevention: Apply rate limiting, access controls, and policy enforcement to stop excessive or unauthorized API activity.
• Automated abuse detection: Distinguish legitimate agent automation from malicious automation and hijacked agents.
• Portfolio fit: Check whether API protection is native or requires a separate product from the same vendor.

The Model Context Protocol has become the common way agents reach tools and data, which means MCP servers are now a real part of the attack surface. Agents register their own MCP connections, so security teams often cannot say which servers are in use, which tools they expose, or who can invoke them. Tool poisoning, tool drift after approval, and over-scoped servers are the specific risks a solution should address.

Evaluation criteria:

• MCP discovery: Find MCP servers and the tools they expose, including shadow servers connected without review.
• Per-agent tool control: Allow or block individual tools per agent rather than approving a server wholesale.
• Call inspection: Inspect requests and responses between agents and tools, tied to the identity behind each call.
• Ongoing trust assessment: Re-evaluate approved tools for drift, unsafe descriptions, and excessive scope.
• Non-MCP integrations: Cover plugin frameworks, custom connectors, and orchestration platforms that do not use MCP.

Best for: Intent-based runtime protection across an agent ecosystem.

Strengths: Intent detection, LLM guards, MCP tool control, audit-ready posture reports.

Things to consider: Broadest API discovery comes from pairing with Radware API Security.

Radware Agentic AI Protection monitors and blocks agent-targeted manipulation across an organization's agent environment. It continuously discovers agents and the tools they can reach across SaaS, home-grown, and end-user environments, and maps their connections and dependencies so security teams can see the ecosystem rather than individual agents.

At runtime it identifies the intent behind agent activity and responds in real time to indirect prompt injection, jailbreaking, and supply chain attacks, using either inline or out-of-band enforcement. Posture management scores and ranks risk across agents and tools, and generates audit-ready reporting aligned to global standards. Coverage extends to developer-hosted coding agents, including Claude Code, running on client-side endpoints. In independent testing, the solution was rated highly effective against indirect prompt injection attacks, blocking up to 95.7% of them.

Key features include:

How it meets the criteria:

Source: Radware

Best for: Securing agents across the full AI lifecycle on one platform.

Strengths: Supply chain scanning, red teaming, runtime protection, MCP traffic control.

Things to consider: Full value assumes commitment to the Palo Alto ecosystem.

Palo Alto Networks Prisma AIRS secures AI agents, applications, models, and data from development through deployment. It discovers AI agents across SaaS platforms, cloud services, low-code tools, and custom environments, and gives centralized visibility into what agents do, what they can access, and how they make decisions.

Before deployment it scans supply chain vulnerabilities in agent artifacts including agent code, MCP servers, and skills, and runs behavior testing using an attack library or a dynamic red teaming agent. It detects over-privileged agents, inventories and validates agent identities, and enforces least-privileged access. At runtime it secures agents against prompt injection and tool misuse, and centrally controls tool calls, LLM interactions, and MCP connections. Portkey's AI Gateway is being integrated into the platform as a unified control plane for agent identity verification and runtime policy.

Key features include:

How it meets the criteria:

Source: Palo Alto Networks

Best for: Governing agent decisions across SaaS, cloud, and endpoints.

Strengths: Layered surface, enforce and protect model with MCP gateway controls.

Things to consider: Coverage centers on major commercial agent platforms.

Zenity is an AI agent security and governance platform built in three layers rather than one. The Surface layer builds a live inventory of agents, evaluates how they are configured, and tests which attack paths are actually exploitable. The Enforce and Protect layers then apply policy and monitor execution, all through the same Boundaries engine.

Rather than inspecting prompts alone, Zenity analyzes what an agent can reach, who it is acting for, and what it is trying to do. It covers agentic SaaS, cloud and home-grown agents, and personal and coding agents on end-user devices. Its MCP Security capability gives security teams a single control point for MCP usage: servers and tools are imported from the Zenity inventory or public registries, every request and tool invocation is visible in real time and tied to an identity, and calls can be allowed, modified, or blocked on live context. For compliance, Zenity maps AI threat techniques to frameworks such as OWASP and MITRE and provides guidance for GDPR, SOX, HIPAA, PCI DSS, FDIC, and the NIST AI RMF.

Key features include:

How it meets the criteria:

Source: Zenity

Best for: Governing AI use from employees and developers through to agents.

Strengths: Shadow AI and shadow MCP discovery, real-time AI firewall, audit logging.

Things to consider: Reporting depth and alert tuning are common review themes.

Prompt Security, part of the SentinelOne Singularity platform, covers every AI touchpoint in an organization: employees, developers, home-grown applications, and autonomous agents. It inventories every AI tool, app, code assistant, and agent in use including unsanctioned shadow AI, and redacts sensitive data and enforces policy in real time across more than 15,000 AI services.

For agents specifically, it maps every agent and MCP server in the environment, automatically discovers shadow MCP servers and unsanctioned agent deployments, and enforces least-privilege access so agents operate only within their defined scope. It keeps a searchable audit log of every agent action, decision, and enterprise system interaction. For AI applications, it tests for prompt injection, jailbreaks, and data poisoning before deployment and turns red teaming findings into production guardrails on a shared policy fabric.

Key features include:

How it meets the criteria:

Source: SentinelOne

Best for: Applying privileged access controls to AI agent identities.

Strengths: Agent discovery with context, task-scoped access, zero standing privileges.

Things to consider: Cost and licensing complexity are recurring review themes.

Idira Secure AI Agents is the agentic capability of the Idira Identity Security Platform, the next-generation identity security platform built on CyberArk's technology and now part of Palo Alto Networks. It extends the privilege controls used for human and machine identities to autonomous AI agents.

It scans SaaS, cloud, and developer environments to identify active agents and enriches each with context such as ownership and permission levels. Once agents are visible, Idira enforces privilege controls such as granting an agent access only for the duration of a specific task, and provides auditability into the actions agents take. The platform manages human, machine, and agentic identities in one control plane that discovers risk, applies privilege dynamically, and governs the lifecycle from first access to final session. It includes governance and reporting across vaults and workloads to satisfy audit and compliance requirements, and has more than 300 out-of-the-box integrations.

Key features include:

How it meets the criteria:

Source: CyberArk

Best for: Securing AI agents through the non-human identities they run on.

Strengths: Agent, MCP server and NHI inventory, Identity Graph, secure provisioning.

Things to consider: Agentless metadata approach does not inspect agent content.

Astrix Security secures AI agents through the non-human identities they operate on, extending identity security to service accounts, OAuth apps, API keys, SSH keys, IAM roles, and agent credentials. It connects to an environment in minutes and automatically discovers AI agents that are custom, third-party, or home-grown, managed or shadow, along with MCP servers, NHIs, and secrets inside and outside vaults.

An Identity Graph shows which platforms and resources each agent can access, who created it, its permissions and tokens, and who else can access it, and a connectivity map correlates data across cloud, SaaS, on-premises systems, databases, vaults, CI/CD, and AI platforms to uncover shadow agents hidden behind tokens and ephemeral sessions. Posture management reduces excessive privileges, fixes configuration weaknesses, and assigns human ownership. Its Agent Control Plane provisions secure-by-design agents with Zero Trust policy at creation, short-lived credentials, and precisely scoped least-privilege access. Astrix is now part of Cisco.

Key features include:

How it meets the criteria:

Source: Astrix Security

Best for: Intent-aware, time-bound access for AI agents.

Strengths: Session-level identities, AI-SPM checks, full chain-of-custody trails.

Things to consider: Newer entrant with limited public third-party validation.

Oasis Agentic Access Management governs how AI agents reach enterprise systems. Policies are defined once and enforced everywhere: the platform understands an agent's intent in real time and applies those policies automatically, blocking risky actions before they reach data. It builds on the wider Oasis NHI Security Cloud, whose capabilities include inventory, ownership, context, posture, and an AI-SPM capability that checks AI agent configurations, permissions, and risk posture.

Each request runs on a short-lived, least-privilege identity, with no standing privilege, long-lived tokens, or hard-coded secrets. Built-in logic and AI determine what an agent is trying to do, break it into a precise action plan, and grant exactly the access required. Every prompt is bound to a unique identity, and every action is linked to a chain of custody covering prompt, intent, policy, session, and action. PAM-style privilege elevation is available, time-bound and triggered only when business context and risk require it.

Key features include:

How it meets the criteria:

Source: Oasis Security

Best for: Governing AI agents inside enterprise identity security.

Strengths: Agent aggregation, ownership with succession, access reviews, MCP server.

Things to consider: Reporting depth and deep configuration draw review criticism.

SailPoint Agent Identity Security brings AI agents, their users, and the tools they access into one governed view as part of SailPoint Identity Security Cloud. It connects directly to AWS, Azure, Google Cloud Platform, Salesforce, Microsoft Copilot Studio, and other platforms to onboard agents automatically, registering each with a unique identity enriched with business and access context.

Each agent is assigned one or multiple human owners, with built-in succession planning so ownership survives role changes, and that ownership data is maintained for audit purposes. Access reviews run regularly to identify and revoke excessive permissions, and also detect when human identities gain entitlements or data access indirectly through agents. The platform governs the service accounts agents use from creation to retirement, surfaces shadow AI by revealing hidden interactions between employees and unmonitored AI tools, and manages human, non-employee, machine, and agent identities in one experience. SailPoint also offers an MCP Server for Identity Security Cloud that lets third-party AI agents interact with the platform through a governed bridge.

Key features include:

How it meets the criteria:

Source: SailPoint