Best Agentic AI Security Tools in 2026

The trade-off is scope. AgentPulse is built around Microsoft 365 and cloud environments. If your agentic AI deployments span multiple clouds, custom LLM infrastructure, or non-Microsoft SaaS platforms, you'll hit the edges of what this tool covers quickly. It's a governance and data security posture tool first. It does not do runtime threat detection, prompt injection blocking, or red teaming. Think of it as the asset management and compliance layer for AI agents, not the detection and response layer.

For a mid-market or enterprise organization that's already using AvePoint for Microsoft 365 data governance, adding AgentPulse is a logical extension. For organizations without that existing AvePoint footprint, the value proposition depends heavily on how Microsoft-centric your AI agent deployments actually are.

Rubrik Rubrik Agent Cloud

Rubrik Agent Cloud does something most agentic AI security tools don't: it lets you undo what an agent did. The Agent Rewind capability is the standout feature. When an AI agent takes a destructive or unauthorized action, whether it deletes records, exfiltrates data, or corrupts a workflow, you can roll it back without downtime or data loss. That's a meaningful operational guarantee that pure monitoring tools can't offer.

The platform covers three distinct functions: discovery and monitoring of agent activity across data, identities, and applications; policy-based governance to define what agents are allowed to do in real time; and remediation through that rollback capability. It integrates with a wide range of enterprise infrastructure including AWS, Google Cloud, VMware, Active Directory, Entra ID, Okta, Salesforce, SAP HANA, and SQL Server. For mid-market and enterprise environments running hybrid infrastructure, that breadth of integration matters. This is not a cloud-only tool.

Rubrik Agent Cloud is part of Rubrik's broader security platform, which already handles data protection, threat analytics, and cyber recovery. If you're an existing Rubrik customer, the agent security capabilities layer on top of infrastructure you've already deployed and trust. If you're not a Rubrik customer, you're evaluating a platform that does a lot more than just AI agent security, and you need to decide whether that's a feature or a complexity tax.

The ideal buyer here is an enterprise with existing Rubrik infrastructure, complex hybrid environments, and a real concern about AI agents taking irreversible actions on critical data. The NIST coverage is broad, spanning asset management, risk assessment, identity controls, data security, continuous monitoring, adverse event analysis, incident mitigation, and recovery. That's a strong compliance story for organizations that need to demonstrate control over AI agent behavior to auditors.

Wallarm Protect Agentic AI

Wallarm Protect Agentic AI comes at the problem from the API security angle, which makes sense because AI agents are fundamentally API consumers. Every action an agent takes goes through an API call. Wallarm's approach is to sit in that traffic flow and enforce controls: blocking prompt injection and code injection attacks, preventing jailbreak attempts against system prompts, enforcing topic boundaries so agents don't go off-script, and stopping sensitive data from leaking through agent responses.

What separates this from a generic WAF or API gateway is the AI-specific threat model. Wallarm understands that the attacker isn't just trying to break the API, they're trying to manipulate the agent's reasoning through the API. The dynamic risk scoring and anomaly detection are designed to catch behavioral drift in agent interactions, not just signature-matched attacks. The rogue agent discovery capability is also worth noting: it detects AI agents accessing your systems that weren't explicitly approved, which is a real problem in organizations where developers are spinning up agents faster than security teams can track.

Deployment is hybrid, which gives flexibility for organizations that can't route all traffic through a cloud proxy. The lack of named integrations in the database is a gap worth flagging: you'll want to validate compatibility with your specific AI infrastructure before committing. The platform supports custom protection policies, which is important because agentic AI use cases vary enormously and a one-size-fits-all policy will generate noise.

Wallarm is the right choice if your primary concern is the API attack surface of your AI agents, particularly prompt injection (OWASP LLM01), unauthorized access through internal APIs, and data leakage through agent outputs. It fits SMB through enterprise. If you're running a security team that already thinks in terms of API threat models, this tool will feel familiar.

Obsidian Security for Salesforce Agentforce

Obsidian Security for Salesforce Agentforce is a narrow tool that does one thing well: securing AI agents inside Salesforce Agentforce. If you're not using Agentforce, this tool is not for you. If you are, it addresses a specific and underappreciated risk: agents that inherit admin-level permissions, bypass the Einstein Trust Layer, or get created outside central governance without anyone knowing they exist.

The shadow agent detection is the most operationally relevant feature. In large Salesforce orgs, it's common for admins and developers to create agents for specific workflows without going through a formal approval process. Obsidian surfaces those agents, identifies who owns them, and flags permission misconfigurations. The supply chain risk detection for connected third-party apps is also meaningful: Agentforce agents often connect to external systems, and each of those integrations is a potential attack vector.

The NIST coverage here is focused: supply chain risk management, asset management, risk assessment, identity and access controls, and continuous monitoring. There's no incident response or recovery capability in this tool. It's a visibility and governance layer, not a detection and response platform. Remediation guidance is actionable but manual.

For organizations running Salesforce at scale, particularly in customer service, sales, or HR automation use cases where Agentforce is being deployed broadly, this fills a real gap that Salesforce's native tooling doesn't cover. The cloud-only deployment model is appropriate given the Salesforce context. The limitation is obvious: single-platform scope. If your AI agent footprint extends beyond Salesforce, you'll need additional tooling alongside this.

Operant AI MCP

Operant AI MCP is the most technically broad tool in this roundup. It covers runtime security for AI applications, APIs, MCP implementations, and Kubernetes workloads in a single platform. The MCP security angle is particularly timely: as Model Context Protocol becomes the standard way for AI agents to connect to tools and data sources, the MCP server and client layer becomes a critical attack surface. Operant provides visibility into MCP registries, whitelist and blacklist management for MCP tools, and non-human identity controls for MCP connections. That's a level of specificity most tools in this space don't have yet.

The API security component handles ghost and zombie API discovery alongside OWASP Top 10 API attack blocking, without requiring VPC mirroring. The in-line auto-redaction of sensitive data as it flows through application stacks is a practical feature for teams worried about PII or secrets leaking through agent interactions. The Kubernetes workload security covers east-west traffic between services, which matters for multi-agent architectures where agents are calling other agents.

The integration list is strong on the AI model provider side: OpenAI, Anthropic, Meta, Cohere, Mistral, Amazon Bedrock, Google Vertex AI, Hugging Face, Snowflake, and Databricks. If you're running a multi-model architecture or evaluating models across providers, Operant has coverage. The Gartner 2025 Market Guide recognition for API Protection and MCP Gateways is a useful external validation for organizations that need to justify tooling decisions to leadership.

The trade-off is complexity. This tool does a lot, and deploying it across AI, API, and Kubernetes layers requires investment in configuration and tuning. It's sized for mid-market and enterprise. If you're a small team looking for a focused solution to one specific problem, the breadth here may be more than you need. But if you're building a cloud-native AI platform and need a single runtime security layer across the whole stack, Operant is worth a serious look.

Adversa AI Agentic AI Security

Adversa AI takes a fundamentally different approach from every other tool in this roundup. Where others focus on runtime controls, governance, or infrastructure isolation, Adversa focuses on finding vulnerabilities before they're exploited. The core capability is continuous red teaming of AI agents, generative AI applications, ML models, and MCP implementations. The goal is to identify real attack vectors, not theoretical ones, and provide actionable hardening recommendations.

The threat modeling and security architecture review services are relevant for organizations that are designing agentic AI systems and want to bake security in from the start rather than bolt it on after deployment. The awareness training component addresses the human side of AI security risk, which is often neglected in tooling-focused conversations. Adversa also publishes research on AI security incidents, which means the platform's threat intelligence is grounded in observed real-world attacks rather than just academic threat models.

The industry coverage is broad: automotive, biometrics, financial services, surveillance, identity and KYC, smart city, and Industry 4.0. That breadth suggests the platform is designed for organizations where AI is embedded in high-stakes operational systems, not just productivity tooling. The NIST coverage reflects this: risk assessment, awareness and training, platform security, continuous monitoring, and adverse event analysis.

The practical limitation is that Adversa is primarily an assessment and validation tool. It will tell you where your AI agents are vulnerable. It does not sit inline and block attacks in real time. For organizations that need both offensive security assessment and runtime protection, Adversa pairs well with a runtime tool like Operant or Wallarm. For organizations that are still in the design and pre-deployment phase of their agentic AI programs, Adversa may be the right first investment before adding runtime controls.

Edera AI Agents

Edera AI Agents solves a problem that most agentic AI security tools ignore entirely: what happens when an AI agent executes code. When an agent generates and runs code, that code needs to run somewhere. If it runs in a shared environment, a malicious or buggy agent can affect other workloads, access data it shouldn't, or escape its intended scope. Edera addresses this with hardware-level isolation and ephemeral sandboxes, giving each agent execution a clean, isolated environment that disappears when the task is done.

The persistent execution capability is important for complex agent workflows that maintain state across extended sessions. Most sandbox solutions force you to choose between isolation and statefulness. Edera supports both. The multi-tenant workload hosting with workload isolation is relevant for organizations building AI platforms that serve multiple internal teams or external customers. The API-based infrastructure management means you can integrate sandbox provisioning into your existing deployment pipelines without manual intervention.

The deployment options are practical: Edera's cloud infrastructure or self-hosted within your own VPC. AWS GovCloud availability and the Carahsoft partnership signal a clear federal market play. If you're in a government or defense context where AI agent code execution needs to meet strict isolation requirements, this is one of the few tools purpose-built for that scenario. Kubernetes integration means it fits into cloud-native infrastructure without requiring a separate orchestration layer.

The NIST coverage is narrower than most tools here: asset management, platform security, and infrastructure resilience. Edera is not a monitoring or detection tool. It doesn't analyze agent behavior or block prompt injection. It provides the secure execution substrate. Think of it as the foundation layer for agentic AI security, not the full stack. Organizations building production AI agent platforms should evaluate Edera alongside a runtime monitoring tool, not instead of one.

How to Choose the Right Tool

The agentic AI security market is fragmented by design. Different tools solve different layers of the problem: governance, runtime protection, secure execution, red teaming, and platform-specific coverage. Buying the wrong one means either paying for capabilities you don't need or leaving a critical gap uncovered. Here's how to think through the decision.

• Start with your deployment context. If your AI agents live primarily in Microsoft 365 or Salesforce Agentforce, platform-specific tools like AgentPulse or Obsidian Security will give you deeper coverage than a generic runtime protection platform. If you're running custom agents on Kubernetes across multiple cloud providers, you need something like Operant or Edera that's built for cloud-native infrastructure.
• Identify your primary threat vector. Prompt injection and API abuse call for a runtime protection tool like Wallarm or Operant. Misconfigured permissions and shadow agents call for a governance tool like AgentPulse or Obsidian. Insecure code execution calls for infrastructure isolation like Edera. Unknown vulnerabilities in your AI architecture call for red teaming like Adversa. Most organizations need more than one layer.
• Decide whether you need detection, prevention, or recovery. Wallarm and Operant block attacks inline. Rubrik Agent Cloud lets you roll back agent actions after the fact. Adversa finds vulnerabilities before they're exploited. AgentPulse and Obsidian give you visibility and governance controls. These are different value propositions and they're not interchangeable.
• Check your compliance requirements before you buy. FedRAMP, SOC 2 Type II, and ISO 27001 certifications matter if you're in a regulated industry or government sector. AvePoint AgentPulse has the strongest compliance certification stack in this roundup. Rubrik has broad NIST coverage across the full incident lifecycle. If your auditors are asking about AI agent controls, map your tool selection to specific NIST CSF categories.
• Evaluate integration depth honestly. A tool that integrates with your existing SIEM, identity provider, and cloud infrastructure is worth more than one that requires a separate workflow. Rubrik's integrations with Active Directory, Entra ID, and Okta are meaningful for identity-centric governance. Operant's coverage of major AI model providers matters if you're running multi-model architectures. Obsidian's single integration with Salesforce Agentforce is a feature if that's your platform, and a hard limit if it's not.
• Consider your team's operational capacity. A tool that generates high-fidelity, low-volume alerts is worth more to a three-person security team than one that requires constant tuning. Adversa's assessment model requires security engineering time to act on findings. Operant's inline blocking requires policy configuration to avoid false positives. Edera's sandbox model is relatively low-maintenance once deployed. Match the operational model to your team's actual bandwidth.
• Think about the build versus buy question for MCP security specifically. Model Context Protocol is becoming the standard interface for agentic AI tool use, and it's a new attack surface that most existing security tools don't cover. Operant has the most explicit MCP security capabilities in this roundup. If your agents are using MCP servers to connect to tools and data sources, MCP gateway security should be a primary selection criterion, not an afterthought.
• Don't ignore the recovery story. Most security tools in this space focus on prevention and detection. Rubrik Agent Cloud is the only tool here with explicit agent action rollback. If your AI agents are operating on critical business data, the ability to undo a destructive action without downtime is a meaningful operational guarantee. Factor recovery capability into your risk model, not just detection and prevention.

Frequently Asked Questions

What is agentic AI security and why does it need dedicated tooling?

Agentic AI security covers the controls needed when AI agents operate autonomously: calling APIs, executing code, accessing data, and taking actions without direct human approval for each step. Traditional security tools weren't designed for non-human identities that make thousands of API calls per minute, inherit permissions dynamically, or can be manipulated through prompt injection rather than credential theft.

What is prompt injection and which tools in this roundup protect against it?

Prompt injection is an attack where malicious input in an agent's environment, such as a web page, document, or API response, causes the agent to take unintended actions or leak sensitive data. It's the OWASP LLM Top 10's top-ranked risk. Wallarm Protect Agentic AI and Operant AI MCP both provide inline blocking for prompt injection attacks.

What are shadow agents and why are they a security risk?

Shadow agents are AI agents deployed without central governance or security review, often created by developers or business users outside formal IT processes. They may inherit excessive permissions, connect to sensitive data sources, or interact with third-party systems without anyone tracking them. Obsidian Security for Salesforce Agentforce and AvePoint AgentPulse both include shadow agent detection capabilities.

Do I need a separate tool for MCP security or does my existing API security cover it?

Existing API security tools generally don't understand MCP-specific constructs like tool registries, MCP server trust relationships, or non-human identity controls for MCP clients. Operant AI MCP is currently the most purpose-built option in this space for MCP gateway security. If your agents are using MCP to connect to tools and data sources, standard API WAF coverage is not sufficient.

Can these tools work together or do they overlap too much?

Most of these tools address different layers and can be combined. A practical stack might pair Edera for secure code execution, Operant or Wallarm for runtime API and prompt protection, and AgentPulse or Obsidian for governance and compliance visibility. Rubrik Agent Cloud adds recovery capability that none of the others provide. Adversa fits as a pre-deployment assessment layer before you add runtime controls.

Which of these tools is best for a small security team with limited operational capacity?

Edera AI Agents has the lowest ongoing operational overhead once deployed, since the sandbox model is largely self-managing. For runtime protection with manageable alert volume, Wallarm's dynamic risk scoring is designed to surface high-confidence threats rather than flooding analysts. Adversa's assessment model requires the most active security engineering time to act on findings.

Conclusion

Agentic AI security is not a solved problem. The tools in this roundup represent the current state of the art, and they're genuinely useful, but the threat landscape is moving faster than the tooling. Prompt injection techniques are evolving. MCP is still being standardized. AI agents are being deployed in production faster than security teams can inventory them. The right approach right now is to pick the layer that represents your highest risk, deploy a tool that addresses it specifically, and build from there. Don't wait for a single platform to cover everything. That platform doesn't exist yet. Browse the full AI security category at /tools to see what else is available, and use /compare to run side-by-side evaluations before you commit.