Securing Agentic AI Workflows in the Enterprise: Top 8 Solutions

TL;DR: Securing agentic AI workflows means governing agent identity, permissions, tool use, and runtime behavior. Exabeam is best for behavior monitoring in the SOC, Microsoft Entra Agent ID is best for agent identity, Prisma AIRS best for platform breadth, Zenity copilot best for governance.

What Are Agentic AI Workflows? 

Agentic AI workflows are systems where artificial intelligence agents perform tasks autonomously, often interacting with various tools, APIs, or data sources to achieve specific objectives. These agents can be configured to operate with varying levels of independence, from handling routine tasks to making complex decisions that would typically require human judgment.

Securing agentic AI workflows in the enterprise requires a defense-in-depth framework that protects every stage from input interpretation to tool execution and output delivery. Unlike static AI chatbots, autonomous agents reason, plan, and invoke enterprise APIs at machine speed, creating complex threat surfaces. Organizations can mitigate these operational risks by implementing core security pillars across their infrastructure.

Common security threats in agentic AI workflows include: 

• Prompt injection
• Indirect prompt injection
• Excessive permissions
• Sensitive data exposure
• Tool and API abuse
• Identity and credential theft
• Insecure output handling
• Supply chain compromise
• Memory manipulation
• Privilege escalation
• Denial of service and resource abuse

Why Agentic AI Workflows Create New Security Risks 

Agentic AI workflows introduce security risks because they combine autonomy, broad system access, adaptive behavior, and reliance on external inputs. These traits make agents powerful for automation, but they also expand the ways errors, misuse, or attacks can lead to unintended actions, data exposure, or compromised business processes.

• Agents can act without continuous human approval: Once launched, agents may access data, execute tasks, or interact with services independently. Without real-time oversight, unintended or malicious actions can be harder to detect and stop.
• Workflows span multiple applications and data sources: Agents often connect to many apps, APIs, and databases, expanding the attack surface. Weak permissions or insecure integrations can allow attackers to compromise broader workflows.
• Agent behavior can change based on context: Agents adapt to real-time inputs and changing objectives, which can make their actions harder to predict. Malicious prompts or altered data may cause unsafe behavior such as privilege escalation or data leakage.
• Compromised inputs can trigger harmful actions: Agents may act on manipulated prompts, data, or external signals. Because they can execute tasks autonomously, compromised inputs can quickly lead to unauthorized or damaging outcomes.

Related content: Read our detailed guide to agentic AI vs. generative AI

11 Common Security Threats in Agentic AI Workflows 

Agentic AI workflows combine autonomous decision-making with access to tools, data, and external systems. This creates security risks that affect both the AI agent and every connected component. Common threats include:

1. Prompt injection: Attackers place malicious instructions in user prompts, documents, web pages, or tool outputs. The agent may follow these instructions instead of its intended task.
2. Indirect prompt injection: An agent processes compromised external content, such as an email or website, that contains hidden instructions. This can trigger unauthorized actions without direct attacker interaction.
3. Excessive permissions: Agents may receive broader access than their tasks require. If compromised or misdirected, they can read sensitive data, modify systems, or execute transactions.
4. Sensitive data exposure: Agents may include confidential information in responses, logs, prompts, or API requests. Data can also leak between users, tools, or workflow stages.
5. Tool and API abuse: Attackers can manipulate an agent into misusing connected tools. Examples include sending unauthorized messages, deleting records, transferring files, or calling restricted APIs.
6. Identity and credential theft: Agentic workflows often depend on API keys, service accounts, and access tokens. Exposed credentials can allow attackers to impersonate the agent or access connected systems directly.
7. Insecure output handling: Agent-generated output may be passed into databases, command shells, browsers, or other applications. Without validation, malicious output can cause injection attacks or unintended system changes.
8. Supply chain compromise: Agents may rely on third-party models, plugins, libraries, or data sources. A compromised dependency can introduce malicious behavior or expose workflow data.
9. Memory manipulation: Attackers may insert false or harmful information into an agent’s long-term memory. The agent can then use this poisoned context in future decisions.
10. Privilege escalation: A compromised agent may exploit weak access controls to obtain additional permissions. This can expand the attack from one workflow component to other systems.
11. Denial of service and resource abuse: Malicious inputs can force agents into repeated tool calls, long reasoning loops, or expensive model requests. This can disrupt operations and increase infrastructure costs.

Related content: Read our detailed guide to LLM security

How AI Security Solutions Can Help Secure Agentic Workflows

Agentic AI security solutions reduce these risks by adding controls before, during, and after an agent executes a task. They discover AI agents across the environment, assign each one a unique identity, enforce least-privilege access, and govern which tools, APIs, and data sources an agent can use. 

Many platforms also inspect prompts, retrieved content, and model responses to detect prompt injection, data leakage, or unsafe output before an agent takes action. Together, these controls limit the impact of excessive permissions, compromised inputs, tool abuse, credential theft, and insecure integrations while providing a consistent security policy across AI workflows.

Modern platforms also provide continuous runtime monitoring instead of relying only on preventive controls. They baseline normal agent behavior, detect anomalies such as unusual tool calls, repeated reasoning loops, privilege escalation attempts, or unexpected data access, and can automatically block, pause, or require human approval for high-risk actions. 

Security teams gain detailed audit logs that show which agent acted, what resources it accessed, and why a decision was made, making investigations and compliance reporting easier. By combining identity, governance, runtime enforcement, and behavioral analytics, agentic AI security solutions help organizations deploy autonomous agents while reducing the likelihood and impact of the threats described above.

Core Components of Agentic AI Security Solutions 

Agent Discovery and Inventory

Maintaining a comprehensive inventory of all active agents is a foundational step in securing agentic AI workflows. Organizations must track not only the existence of each agent but also its capabilities, assigned tasks, and integration points. This visibility enables security teams to assess risk, monitor agent proliferation, and quickly identify unauthorized or rogue agents. Without a reliable inventory, it is impossible to enforce consistent security policies or respond effectively to incidents involving autonomous agents.

Regular audits and automated discovery tools are necessary to keep this inventory current, especially in dynamic environments where agents may be created or decommissioned frequently. Integrating agent discovery with broader asset management systems helps ensure that new agents do not go unnoticed and that decommissioned agents are properly retired. This approach reduces the risk of orphaned agents that retain access to sensitive systems or data, which could be exploited by attackers.

Agent Identity Management

Assigning unique, verifiable identities to each agent is critical for accountability and access control. Agent identity management involves issuing credentials, maintaining authentication mechanisms, and tracking agent actions over time. By tying every action to a specific agent identity, organizations can audit behavior, enforce policy, and detect anomalies more effectively. Weak or shared identities make it difficult to determine responsibility and increase the risk of unauthorized actions going undetected.

Implementing robust identity lifecycle management—including provisioning, rotation, and revocation of agent credentials—is essential for minimizing exposure. This process should be integrated with existing identity and access management (IAM) systems to ensure consistency across human and non-human actors. Strong identity management also supports granular access control, making it possible to limit agent permissions based on their role, function, or risk profile.

Least-Privilege Access Controls

Enforcing the principle of least privilege is especially important in agentic AI workflows, where autonomous agents often require access to sensitive resources. Each agent should be granted only the minimum permissions necessary to perform its assigned tasks, reducing the potential impact if the agent is compromised or behaves unexpectedly. Overly broad permissions increase the risk of data breaches, privilege escalation, and lateral movement by attackers.

Configuring least-privilege access requires a detailed understanding of agent workflows, resource dependencies, and integration points. Permissions should be regularly reviewed and adjusted as agent roles evolve or as new threats are identified. Automated tools can help enforce access policies and detect violations, but human oversight remains essential to ensure that privilege boundaries are respected and updated in response to changing requirements.

Tool and API Governance

Agentic AI workflows depend on external tools and APIs to perform actions. Every connected tool should be treated as a high-risk integration because it extends the agent’s capabilities beyond generating text. Organizations should approve which tools agents can use, define allowed operations, and restrict access to only the APIs required for each workflow. Unnecessary integrations should be removed to reduce the attack surface.

Security teams should also validate every tool request before execution. This includes checking parameters, enforcing rate limits, and blocking requests that fall outside approved policies. Detailed logging of API calls provides an audit trail for investigations and helps identify abnormal behavior, such as unexpected tool usage or repeated failed requests that may indicate an attack.

Data Access Controls

Agents often process confidential business information, customer records, and internal documents. Data access controls ensure agents can retrieve only the information required for their current task. This reduces the likelihood of sensitive data exposure if an agent is compromised or produces an unintended response.

Access decisions should consider both the agent’s identity and the context of the request. Organizations should combine role-based or attribute-based access controls with data classification, encryption, and masking where appropriate. Access should be continuously reviewed, and permissions should be revoked immediately when an agent, workflow, or integration is no longer needed.

Runtime Behavior Monitoring

Agent security cannot rely only on configuration because agent behavior changes during execution. Runtime monitoring provides continuous visibility into agent decisions, tool usage, permission requests, and interactions with users and external systems. This makes it possible to detect suspicious activity before it results in significant damage.

Effective monitoring combines logs, behavioral analytics, and automated alerts to identify anomalies such as unusual tool calls, unexpected data access, repeated reasoning loops, or attempts to exceed assigned permissions. When high-risk behavior is detected, organizations should be able to pause the workflow, require human approval, or terminate the agent until the activity has been investigated.

Prompt and Response Inspection

Every prompt sent to an agent and every response it generates represents a potential security boundary. Prompt inspection helps identify malicious instructions, prompt injection attempts, and sensitive information before the agent processes the request. Response inspection verifies that generated output does not expose confidential data, unsafe commands, or content that violates organizational policies.

Inspection should occur throughout the workflow rather than only at user input. Prompts received from connected tools, retrieved documents, websites, and APIs should be treated as untrusted content and validated before reaching the model. Likewise, responses should be scanned before being passed to downstream systems, reducing the risk that unsafe output triggers harmful actions in applications, databases, or automated processes.

Agentic AI Security Solutions At a Glance

The table below summarizes the key differences between the solutions covered in this section, including where each one fits and what to weigh before adopting it. Each solution is explored in more detail below.

Category	Solution	Best For	Key Strengths	Things to Consider
Enterprise security platforms extended to AI agents	Exabeam Agent Behavior Analytics (ABA)	Monitoring AI agent behavior inside security operations	Behavioral baselining across major AI platforms and custom agents	Detection and investigation focus rather than runtime blocking
Enterprise security platforms extended to AI agents	Microsoft Entra Agent ID	Giving AI agents managed enterprise identities	Extends Conditional Access and governance to agent identities	Requires Agent 365 or Microsoft 365 E7 licensing
Enterprise security platforms extended to AI agents	CrowdStrike Falcon Shield	Discovering AI agents across SaaS applications	SaaS posture checks plus AI agent and identity discovery	Coverage centers on SaaS rather than custom agents
Enterprise security platforms extended to AI agents	CyberArk Secure AI Agents	Applying privilege controls to AI agent identities	Task-scoped access through an AI Agent Gateway	Identity-first scope rather than prompt or model security
Dedicated AI security platforms	Palo Alto Networks Prisma AIRS	Securing AI apps, models, data, and agents in one platform	Discovery, red teaming, and runtime enforcement in one place	Some capabilities depend on the Palo Alto ecosystem
Dedicated AI security platforms	Zenity	Governing copilots and low-code agents in business platforms	Analyzes full agent execution paths, not just prompts	Coverage weaker for open-source agent frameworks
Dedicated AI security platforms	Noma Security	Securing homegrown AI apps, agents, and MCP servers	Discovery through runtime enforcement with red teaming	Less depth on SaaS copilot platforms
Dedicated AI security platforms	HiddenLayer AI Security Platform	Protecting AI models and the agents built on them	Model scanning, attack simulation, and runtime firewall	Centered on the model layer rather than agent identity

Notable Agentic AI Security Solutions 

How we selected these solutions: We shortlisted agentic AI security solutions based on agent discovery and inventory, agent identity and least-privilege access controls, tool and API governance, runtime behavior monitoring, and prompt and response inspection.

Enterprise Security Platforms Extended to AI Agents

1. Exabeam Agent Behavior Analytics (ABA)

Best for: Monitoring AI agent behavior inside security operations

Strengths: Behavioral baselining across major AI platforms and custom agents

Things to consider: Detection and investigation focus rather than runtime blocking

Exabeam extends user and entity behavior analytics to AI agents and autonomous workflows through Agent Behavior Analytics. ABA establishes behavioral baselines for each agent to detect misuse, drift, abnormal tool use, risky access, and activity outside an agent’s expected role. By linking pre-deployment verification, runtime telemetry collection, and secure tool connectivity, the platform provides end-to-end security for agentic workflows.

The solution integrates with open-source frameworks, commercial AI engines, and custom enterprise deployments. Security teams can run ABA on top of their existing SIEM via New-Scale Analytics, or deploy it within New-Scale Fusion to correlate human and machine identities in a single interface.

Key features include:

• Behavioral detections for AI agents: Baselines agent behavior and detects autonomous installation and execution, tool usage and MCP activity, risky access, and activity outside an agent’s expected role.
• Open agent telemetry: Analyzes activity from Claude, ChatGPT, Google Gemini, Microsoft Copilot, and GitHub Copilot, plus any agent that shares open telemetry. Observra standardizes agent-native telemetry using a Common Information Model, and Exabeam Agent Sensor collects and normalizes activity across models and runtimes.
• Pre-deployment verification (Praxen): Uses the open-source Praxen framework to compare declared agent policies with code, configuration, and logs before deployment, catching excessive permissions and configuration gaps.
• Runtime agent telemetry (Observra): Employs the open-source Observra SDK to capture and normalize runtime telemetry, including model calls, tool execution, and token usage, across multiple development frameworks.
• OWASP Agentic Top 10 coverage mapping: Outcomes Navigator maps ABA coverage to the OWASP Agentic Top 10, identifies gaps in data sources and detections, and uses the Exabeam Nova Advisor Agent to recommend content that improves readiness.
• Machine-built investigation timelines: Correlates agent actions with related user, entity, and application activity. Exabeam Nova summarizes threats, explains behavioral context, and recommends next steps while analysts remain in control.
• Editable detection content: ABA detections are available in Threat Detection Management for review, cloning, and tuning. Exabeam Nova Rule Creator builds behavioral models and correlation rules from natural language and converts Sigma and YARA rules into Exabeam detections.
• Flexible deployment: New-Scale Analytics adds behavioral analytics to an existing SIEM, and New-Scale Fusion provides integrated detection, investigation, and response across human and non-human identities.

Limitations (as reported by users on G2 for the New-Scale platform that ABA runs on):

• Search usability: Some reviewers find cloud search less intuitive than other parts of the platform.
• Parsing and field mapping: Users report that log parsing and manual data field mapping take additional setup effort.
• Documentation depth: A few reviewers would like more detail in product support documentation.

Source: Exabeam 

2. Microsoft Entra Agent ID

Best for: Giving AI agents managed enterprise identities

Strengths: Extends Conditional Access and governance to agent identities

Things to consider: Requires Agent 365 or Microsoft 365 E7 licensing

Microsoft Entra Agent ID applies Microsoft Entra identity and access management to AI agents. Agents are inventoried and provisioned with agent identities that support authentication, policy enforcement, and integration with existing organizational policies, and all agent activity is logged for audit.

The product covers four areas: managing agent identities at scale, governing agent identity sponsorship and lifecycle, protecting agent access to resources, and applying network-level controls to agent traffic. Agent ID capabilities are included for agents managed by Microsoft Agent 365, Microsoft’s control plane for agents, and are available through the Agent 365 and Microsoft 365 E7 plans.

Key features include:

• Agent identity provisioning at scale: Assigns agent identities so each agent has built-in authentication and policy enforcement. Blueprints, tasks, metadata, and logs are managed centrally in the admin center.
• Conditional Access for agents: Enforces real-time policies for agent access to resources, can block risky agents, and supports granular access control policies based on configured security attributes.
• Identity governance for agents: Automates governance from deployment to expiration, ensures sponsors are assigned and maintained, and makes access assignments auditable and time bound.
• Identity protection for agent activity: Detects and flags unusual or unauthorized agent activity, traces agents with compromised tokens, and supports remediation of compromised agents.
• Network controls: Logs agent network activity for audit and threat detection, applies web categorization to APIs and MCP servers, restricts file uploads and downloads, and blocks malicious destinations automatically.
• Least-privilege access to resources and other agents: Provisioned agent identities are managed with least-privilege access to apps, resources, and other agents.

Limitations (as reported by users on G2 for Microsoft Entra ID, the parent platform Agent ID sits within):

• Licensing complexity and cost: Reviewers describe the licensing structure as complex and costly, particularly for advanced capabilities.
• Configuration learning curve: Users report a steep initial configuration curve and a dense interface built primarily for IT professionals.
• Non-Microsoft coverage: Some reviewers note limited compatibility with non-Microsoft services and difficulty integrating legacy applications.
• Reporting and customization: Advanced reporting and customization options are described as somewhat limited.
• Alert volume: Continuous monitoring can generate a high volume of notifications.

Source: Microsoft

3. CrowdStrike Falcon Shield

Best for: Discovering AI agents across SaaS applications

Strengths: SaaS posture checks plus AI agent and identity discovery

Things to consider: Coverage centers on SaaS rather than custom agents

CrowdStrike Falcon Shield is the SaaS security component of the Falcon platform. It connects to SaaS applications to surface misconfigurations, enforce governance, detect threats, and trigger response actions from a single place.

For agentic workflows, Falcon Shield discovers AI agents across SaaS platforms including Microsoft 365, Salesforce, and OpenAI, maps the systems each agent can access, detects risky behavior, and contains threats. It also covers human and non-human identities, flagging accounts that are over-permissioned, high risk, dormant, or partially deprovisioned.

Key features include:

• AI agent discovery across SaaS: Finds AI agents inside SaaS platforms such as Microsoft 365, Salesforce, and OpenAI, maps their system access, and detects risky behavior before automation is exploited.
• Non-human identity coverage: Identifies human and non-human identities that are over-permissioned, high risk, dormant, or partially deprovisioned.
• Configuration hardening checks: Applies more than 3,500 application hardening and configuration checks, with support for custom security checks across the SaaS stack.
• Threat detection and automated response: Delivers real-time alerts and automated responses for suspicious user behavior, login anomalies, and device issues.
• Shadow application discovery: Correlates real user activity with posture integrations and identity context to surface sanctioned, unsanctioned, and connected applications.
• Broad SaaS integration: Ships with more than 200 integrated SaaS applications out of the box, connecting apps to surface misconfigurations in minutes.

Limitations (as reported by users on G2):

• Ticketing automation: Reviewers report that ticketing automation is limited, which affects workflow efficiency.
• Integration effort: Users note that many integrations take considerable work to set up, and some are still being built out.
• Role-based access granularity: Some reviewers want more granular role-based access controls, since certain views are either too broad or too restrictive for a given role.

Source: CrowdStrike

4. CyberArk Secure AI Agents

Best for: Applying privilege controls to AI agent identities

Strengths: Task-scoped access through an AI Agent Gateway

Things to consider: Identity-first scope rather than prompt or model security

CyberArk Secure AI Agents applies privilege controls to AI agents as a distinct identity class. It discovers agents across SaaS, cloud, and developer environments and enriches each one with context such as ownership, purpose description, status, and permissions, so teams can see who owns an agent, what it does, and what it can reach.

Access is enforced through an AI Agent Gateway that sits between agents and the tools they use. Permissions are granted for a specific task at a specific privilege level and revoked automatically, which is how the solution works toward zero standing privileges. Agent actions and communications are logged, so an audit shows which human user initiated an agent, which agent identity acted, which tools ran, and which resource was touched.

Key features include:

• Discovery and context: Discovers AI agents across SaaS, cloud, and developer environments and records ownership, purpose, status, and permissions for each one.
• AI Agent Gateway: Acts as an enforcement point between agents and their tools, granting task-scoped permissions at the right privilege level and revoking them automatically.
• Lifecycle management and audit logging: Logs agent actions and communications, giving visibility into what was performed, by which agent identity, and on behalf of which user.
• Threat detection and response: Flags abnormal agent behavior and allows an agent to be suspended immediately, with associated services disabled or shut down when an agent exceeds its role.
• Agent Watch observability framework: An open source module that monitors LLM and tool calls, generates graphs of agent interactions, and logs tool inputs, arguments, and performance data across multiple AI frameworks.
• Agent Guard secrets provisioning: An open source module that populates API keys and secrets as environment variables, with support for providers including CyberArk Secrets Manager and AWS Secrets Manager.

Limitations (as reported by users on G2 for CyberArk Privileged Access Manager, the platform this solution builds on):

• Architecture and deployment complexity: Reviewers describe a complex architecture that takes effort to design and establish.
• Learning curve: Users report steep administrator and end-user learning curves that can slow adoption.
• Upgrades and resilience: Some reviewers note that upgrades are not straightforward and that redundancy occasionally needs manual intervention after a failure.
• Cost: Price is raised as a concern by multiple reviewers.
• Support model: A few users say the support model could be improved.

Source: CyberArk

Dedicated AI Security Platforms

5. Palo Alto Networks Prisma AIRS

Best for: Securing AI apps, models, data, and agents in one platform

Strengths: Discovery, red teaming, and runtime enforcement in one place

Things to consider: Some capabilities depend on the Palo Alto ecosystem

Prisma AIRS is Palo Alto Networks’ platform for discovering, assessing, and protecting an AI ecosystem from development through deployment. It provides visibility into every AI agent, application, and model and how they connect across an environment, continuously tests AI apps and agents, controls permissions, and monitors overall security posture in real time.

For agentic workloads, Agent Security verifies every agent identity and enforces real-time controls to stop unauthorized actions as deployments scale from pilot to production. The AI Gateway serves as a control plane for discovering, governing, and securing enterprise AI activity, and recent releases have added multi-turn attack support, agentic target profiling, and red teaming for multi-agent systems.

Key features include:

• AI Gateway: Provides a single control plane to discover, govern, and secure enterprise AI activity.
• Agent Security: Verifies agent identities and enforces real-time security to block unauthorized agent actions across the agent ecosystem.
• AI Red Teaming: Simulates real-world attacks against AI agents and applications to find and fix weaknesses before runtime, including multi-turn attacks and tests against autonomous and multi-agent systems.
• AI Runtime Security: Monitors AI behavior and enforces real-time safeguards against manipulation, data exposure, and unsafe actions during live AI interactions.
• AI Model Security: Scans third-party models for vulnerabilities such as model tampering, malicious scripts, and deserialization attacks.
• AI Posture Management: Provides visibility and control over AI data used for training or inference, the integrity of AI agents and apps, and access to deployed models.

Limitations (based on publicly available sources):

• Ecosystem coupling: The platform is most closely integrated with existing Palo Alto Networks investments, and some capabilities rely on Palo Alto network appliances or cloud agents.
• Configuration effort: The breadth of the platform means a large integration and configuration footprint for the security team.
• Pricing transparency: Pricing is quote-based with no public tier, and the product is scoped for enterprise buyers.
• Preview capabilities: Parts of the agent portfolio, including the AI Agent Gateway, have been released in limited preview rather than general availability.
• Automated testing limits: Automated red teaming does not fully replace expert human testing for the highest-risk deployments.

Source: Palo Alto Networks 

6. Zenity

Best for: Governing copilots and low-code agents in business platforms

Strengths: Analyzes full agent execution paths, not just prompts

Things to consider: Coverage weaker for open-source agent frameworks

Zenity is an AI agent security and governance platform built around the agent’s decision rather than the prompt alone. It examines the full execution path, including tool calls, memory access, data usage, and control flow, to identify malicious or unintended outcomes even when the inputs look harmless.

The platform is organized in three layers. Surface builds a live inventory of agents, evaluates how they are configured, and tests what is actually exploitable. Enforce and Protect then apply policy and runtime controls. Coverage spans agentic SaaS platforms such as Salesforce Agentforce and Copilot Studio, home-grown agents on platforms including AWS Bedrock and Google Vertex AI, and personal and coding agents running on endpoints.

Key features include:

• AI Observability: Builds a live inventory of agents across SaaS, custom, and endpoint deployments and tracks the data each one touches.
• AI Security Posture Management: Evaluates agent configuration and permissions against policy before an agent goes live.
• AI Exposure Management: Validates which of an agent’s attack paths are genuinely exploitable, scores each one, and produces a fix that can be applied in Runtime Boundaries.
• Runtime Boundaries and AIDR: Applies runtime policy enforcement and agent detection and response across SaaS, cloud, and endpoint environments.
• Agentic identity and MCP security: Covers agent identity and the MCP connections agents use to reach external tools.
• Execution path analysis: Examines tool calls, memory access, data usage, and control flow together, exposing attacks that prompt-based firewalls miss.

Limitations (based on publicly available sources):

• Platform coverage boundaries: Purpose-built support centers on Microsoft, Salesforce, ServiceNow, and ChatGPT Enterprise; agents built on open-source frameworks such as LangChain or inside data platforms are less covered.
• No offensive testing module: The platform is oriented toward detection and prevention and does not provide automated red teaming or adversarial probe libraries.
• Developer toolchain depth: Coverage of endpoint developer agents is described as surface-level monitoring, without securing MCP tool execution itself.
• Deployment options: The platform is delivered as SaaS, with no documented on-premises or self-hosted option.
• Remediation workflow: Findings still require human interpretation, with no built-in path to push results into CI/CD pipelines.

Source: Zenity

7. Noma Security

Best for: Securing homegrown AI apps, agents, and MCP servers

Strengths: Discovery through runtime enforcement with red teaming

Things to consider: Less depth on SaaS copilot platforms

Noma Security discovers, governs, and protects AI and agents across the enterprise, covering homegrown AI, SaaS agents, and coding assistants. Its discovery layer maps every model, agent, MCP server, and data source, along with how they connect, so risk is assessed against the full dependency chain rather than isolated components.

The platform then sets boundaries before deployment: an approved AI supply chain of authorized models, tools, and MCP servers; identity and access controls for every AI application and agent; and action policies that govern autonomous behavior. At runtime it inspects every prompt, response, and tool call, applying security policies for threats such as prompt injection and jailbreaks, privacy policies that block sensitive data from leaving the environment, and compliance policies that must pass before an agent acts.

Key features include:

• AI and agent discovery: Maps models, agents, MCP servers, and data sources and shows how they connect across the AI landscape.
• Agentic Access Control: Applies policy-based approval, runtime enforcement, and continuous monitoring to agent access.
• AI Security Posture Management: Identifies risks across AI environments and establishes an approved supply chain of models, tools, and MCP servers.
• Red Teaming: Runs offensive testing for prompt injection, jailbreaks, and data leakage, with continuous validation as systems change.
• Runtime Protection: Monitors all AI and agentic communication as it happens and enforces security, privacy, and compliance policies at runtime.
• MCP server security: Provides threat detection across agents and the MCP servers they depend on.

Limitations (based on publicly available sources):

• Business platform depth: Coverage is strongest for homegrown AI, RAG, and MCP estates; depth on SaaS copilot platforms is narrower than platform-specific competitors.
• Containment model: Runtime actions block or disallow connections rather than running each agent inside an isolated execution environment.
• Compliance evidence: EU AI Act and ISO 42001 evidence automation is not documented as a product capability.
• Gateway and cost visibility: Gateway-level policy enforcement in self-hosted stacks relies on an external AI gateway plugin, and usage or cost observability is limited.
• Pricing transparency: Pricing is enterprise quote-based with no published tiers.

Source: Noma Security

8. HiddenLayer AI Security Platform

Best for: Protecting AI models and the agents built on them

Strengths: Model scanning, attack simulation, and runtime firewall

Things to consider: Centered on the model layer rather than agent identity

HiddenLayer’s AI Security Platform secures agentic, generative, and predictive AI applications across their lifecycle. It is built on four modules: AI Discovery, which inventories AI applications, models, and assets; AI Supply Chain Security, which validates model integrity before deployment; AI Attack Simulation, which continuously tests defenses; and AI Runtime Security, a firewall that monitors and responds to attacks in production.

For agentic workloads, the platform protects autonomous and tool-using systems from misuse, privilege escalation, and cross-system exploitation, and enforces policy-based guardrails against prompt injection, data leakage, and unsafe behavior in real time. Deployment is simplified through pre-built integrations into CI/CD, MLOps, data pipelines, and SIEM or SOAR tooling.

Key features include:

• AI Discovery: Identifies and builds an inventory of the AI applications, models, and assets in an environment to remove shadow AI.
• AI Supply Chain Security: Analyzes and protects AI applications, models, and assets during development, validating integrity before deployment.
• AI Attack Simulation: Continually identifies threats and validates defenses across agentic and generative AI applications at scale.
• AI Runtime Security: A firewall that monitors, detects, and responds in real time to adversarial threats against agentic and generative AI applications.
• AI guardrails: Enforces policies that prevent prompt injection, data leakage, and unsafe AI behavior as it occurs.
• Agentic and MCP protection: Safeguards autonomous and tool-using systems against misuse, escalation, cross-system exploitation, and rogue behavior.
• Model scanning: Detects malicious models, backdoored weights, and vulnerable dependencies before models are deployed.

Limitations (based on publicly available sources):

• Scope center of gravity: Core strength sits at the model artifact and inference layer rather than agent identity, permissions, or business-platform posture.
• Newer agentic capabilities: Agent runtime capabilities were introduced in 2026 and are adopted in stages, starting with visibility and detection before full enforcement.
• Pricing transparency: There is no public pricing; quotes are scoped by model count, deployment size, and selected modules.
• Enterprise deployment orientation: The platform is built for enterprise deployment, which adds procurement and rollout overhead for smaller teams.
• Complementary tooling: Teams that need deep identity governance or copilot posture management typically pair it with additional tools.

Source: HiddenLayer

Conclusion

Securing agentic AI workflows requires a multi-layered approach that addresses the unique autonomy of AI agents. By implementing robust identity management, least-privilege access, and continuous runtime monitoring, organizations can effectively mitigate emerging risks. Maintaining visibility into agent behavior and governing tool integrations ensures that automation remains safe and accountable. This comprehensive framework allows enterprises to harness the power of autonomous AI while maintaining a strong security posture.