Do you actually know whether your organization’s AI agents have more access than they need, or are you just hoping they don’t?
A new report from Enterprise Management Associates turned up the surprising revelation that 94% of enterprises expressed confidence that their artificial intelligence agents do not have a higher level of access than what’s needed to accomplish the tasks they’re being asked to do. However, that confidence isn’t matched by real enforcement. The same report found that just 33% of organizations actually enforce agentic access using real security controls.
The finding suggests there’s a significant disconnect between the assumption of security and real enforcement, and it’s a disconnect that could leave organizations exposed. They’re increasingly relying on autonomous AI agents to automate various business tasks that were once executed by humans, yet most are simply assuming those agents won’t overstep their boundaries, rather than taking real steps to enforce what they can and cannot access.
This is part of a broader pattern of innovation outpacing enterprise security, and in many environments, agent access is where the phenomenon shows up most concretely.
Many aren’t sure how to go about securing an AI agent, so we’ve evaluated five of the strongest platforms for the job, each taking a genuinely different approach: Zscaler, TrueFoundry, Check Point, Obsidian Security, and Astrix Security. Each of these solutions is designed to secure AI systems by safeguarding employees’ use of AI applications, governing agent behavior, monitoring Model Context Protocol traffic, and defending AI applications from prompt injection attacks and data breaches.
Key Takeaways
- No single platform can narrow the distance between confidence and enforcement by itself. The strongest security postures make use of several approaches.
- Some agentic security systems extend an organization’s existing infrastructure. Examples include Zscaler’s Zero Trust Exchange, which has added protections for AI agents, and Check Point, which bolsters the firewalls its customers already rely on. TrueFoundry takes a different approach, having built a dedicated AI gateway as a standalone product.
- Network-based, lifecycle-based and identity-based approaches to agentic security catch different types of failures, rather than using different methods to solve the same problems.
- Securing MCP alone isn’t enough to protect AI agents, as it’s actually just one of many attack surfaces.
How We Compared Them
Each platform was scored across five criteria: discovery and visibility (finding active agents, including shadow AI); identity and access control (how permissions are managed and verified); real-time enforcement (the ability to monitor, intercept, and block threats like adversarial prompts and data theft); infrastructure fit (whether it extends existing tools or requires a standalone deployment); and MCP/tool-specific coverage (visibility into MCP traffic and other ways agents call third-party tools).
Real-time enforcement and identity and access control were weighted most heavily, since these determine whether a platform can actually stop a bad action rather than just flag it after the fact. Discovery and visibility and MCP/tool-specific coverage carried moderate weight, while whether a platform extends existing infrastructure was treated as a secondary factor, since it affects ease of adoption rather than security outcomes directly.
| Criterion | Zscaler | TrueFoundry | Check Point | Obsidian Security | Astrix Security |
| Discovery & visibility | 4 | 3 | 4 | 4 | 5 |
| Identity & access control | 3 | 5 | 4 | 3 | 5 |
| Real-time enforcement | 4 | 4 | 5 | 3 | 3 |
| Extends existing infrastructure | 5 | 2 | 5 | 2 | 2 |
| MCP/tool-specific coverage | 3 | 5 | 4 | 2 | 3 |
1. Zscaler
Zscaler unveiled its approach to agentic security in June 2026 when it expanded the capabilities of its Zero Trust Exchange platform with new features such as AI Broker, AI Access Graph and Endpoint AI Security. This means organizations can monitor and govern AI traffic using the same infrastructure that already safeguards their cloud and on-premises environments.
Zscaler does this by comparing agents’ behaviors and communications with an organization’s existing zero-trust policies. It’s built to intercept malicious traffic before anything leaves the network and reaches an external AI model or API, using data loss prevention controls to prevent employees from uploading sensitive information.
Pros: By building on its existing Zero Trust infrastructure, Zscaler’s agentic security enhancements make life easier for companies that already use its platform as their primary security tool, simplifying deployment.
Cons: The biggest downside is that it only provides full value to companies that are already part of the Zscaler ecosystem. Organizations that aren’t already running Zscaler would need to adopt its broader platform first to access these agent protections.
Best for: Companies that already rely on Zscaler’s Zero Trust platform and need an easy way to secure AI agents.
2. TrueFoundry
TrueFoundry has developed an AI Gateway tool designed to secure MCP traffic and other modes of calling third-party tools. It relies on its OAuth 2.0 Identity Injection capabilities to verify that the person who triggered an agent’s action has valid permission to access the tool or the dataset in question, so organizations don’t have to run the risk of giving any agent broad access privileges. In this way, each agent acts as a direct proxy of its user.
The platform combines OAuth 2.0 with MCP inspection and LLM routing to centralize this control within a single product.
Pros: Protects against a critical weakness that’s invisible to network- and identity-based platforms, namely the risk that an AI agent is granted greater privileges than the human user who controls it. It also simplifies tool governance by bundling LLM, MCP and agent gateway functionality into a single product.
Cons: TrueFoundry’s product is a separate gateway, so it can only monitor agent traffic that passes directly through it. Any agent action that bypasses the gateway entirely goes unmonitored.
Best for: Businesses that are most concerned about agents being granted excessive privileges that could cause downstream damage.
3. Check Point
Check Point’s philosophy for agent security is to extend the capabilities of the existing firewalls that organizations have already deployed. It’s designed to secure three key vulnerabilities relating to AI agents – employee use of AI tools, agentic traffic such as MCP calls, and the risk of malicious prompts designed to get around AI model safeguards to exfiltrate sensitive data or perform unauthorized actions.
Check Point’s prompt-injection defenses are built on technology from Lakera, an AI security startup it acquired last year. It acts like an intelligence engine that operates within the existing firewall to monitor AI traffic. The AI firewall leverages Check Point’s existing Identity Awareness tech to apply policy enforcement in real time to protect against AI threats, without requiring organizations to undergo the hassle of installing new hardware or management consoles.
Pros: No need to install separate hardware or set up a new dashboard. Instead, it allows organizations to monitor and safeguard their agents’ traffic through the same policy layer that covers their existing AI applications and employee AI usage. It leverages Check Point’s highly regarded Identity Awareness capability.
Cons: The firewall can only act on traffic that actually crosses the network it’s deployed on, so how much of your AI environment it covers depends on your network architecture.
Best for: Enterprises that need a way to enhance their existing firewall infrastructure with AI agent protections, instead of setting up new hardware and tools specifically for this.
4. Obsidian Security
Obsidian Security provides a highly specific capability by protecting software-as-a-service environments, which is an attack surface that’s often overlooked by other tools. Rather than monitoring network traffic or MCP tool calls, it tries to identify which AI agents have access to an organization’s SaaS applications and data.
It’s an important capability because AI agents are increasingly being given access to platforms like Salesforce and Google Workspace to automate business tasks, which means they now hold sensitive API keys and data. By mapping their permissions, Obsidian Security discovers which agents have access to what, before applying posture management controls in the SaaS layer to safeguard their interactions with different applications.
Pros: Obsidian is specifically designed to protect the SaaS-specific blind spot that none of the other platforms on this list address. It also has a long list of named enterprise customers, enhancing its credibility.
Cons: Because it’s focused specifically on the SaaS layer, organizations will need additional tools to protect AI agents at the network and identity layers.
Best for: Organizations that have valid concerns about AI agents accessing sensitive data within SaaS applications, which sit outside the scope of standard network firewalls.
5. Astrix Security
Astrix Security treats agentic security as an identity problem. Instead of looking at network packets and the prompts sent to AI models, it sets out to discover and map all of the AI agents, MCP servers and service accounts running within the customer’s environments, including the public cloud, SaaS and identity and access platforms. Each one is given a non-human identity, which means it’s specifically designated as an AI system.
By doing this, it can apply strict least-privilege access controls, ownership tracking, lifecycle management and short-lived access credentials for each non-human entity. It can also identify and remove excessive privileges granted to AI agents. Once an agent completes a task, any credentials it was given to do that job are immediately revoked in order to prevent access privileges from being accumulated over time.
Pros: It puts a strong emphasis on agent lifecycle management and the revocation of access credentials when the agent no longer needs them, which is a capability not found in the other platforms we’ve evaluated.
Cons: Astrix operates specifically at the identity layer, meaning it does not inspect agentic traffic through the corporate network. As such, it cannot see or block AI-specific attacks like adversarial prompts. It’s most effective when used alongside network-based AI security tools.
Best for: Enterprises that are worried about the risk of AI agents accumulating access privileges and security credentials over time, which would represent a prime target for malicious actors.
Final Thoughts on Securing AI Agents
AI agents are vulnerable to all kinds of different risks, and with just 33% of organizations actively enforcing the access policies designed to restrict their behavior, it’s clear that turning that confidence into real enforcement is going to take a lot of work.
For many, it’s not enough to choose a single winner from the above list of AI security tools. To ensure robust agentic AI security, enterprises must recognize that network-based enforcement, tool-call and MCP monitoring, SaaS-specific and identity-focused approaches each protect against risks that the others fail to address.
