AI agent security for the enterprise

The security problem AI agents create

Enterprise security programs are built on a principle that AI agents break entirely. You cannot protect what you cannot see, and you cannot trust what you cannot control.

Agents running on Microsoft Foundry, Copilot Studio, Google Gemini Enterprise Agent Platform, and Zoho Zia are not in the enterprise directory. They accumulate access with no named owner, no security controls configured, and no enforcement point between them and the back-end systems they reach. When an agent calls an API, reads a file store, or writes a record, that action typically executes with nothing evaluating it first.

Research published in early 2026 found that 88% of organizations reported AI agent security incidents, yet only 22% treat agents as identity-bearing entities that require security controls. The agents in your environment today will not appear in your next access review. If one is misconfigured, over-privileged, or targeted, the first signal is likely to be the damage it causes.

SecureAI is an AI governance and security platform that treats AI agents as a new identity type alongside your workforce, connecting to the AI platforms your organization runs, continuously surfacing every agent and the security signals associated with it. It enforces access and content policy at the MCP gateway on every tool call before it reaches the backend.

How ManageEngine SecureAI addresses AI agent security

Because SecureAI deploys on-premise, the data this security program generates — inventory records, policy decisions, gateway activity logs — never leaves your infrastructure.

SecureAI's agentic AI security capabilities are organized around four functions: detecting what exists and which agents carry risk, assessing the scope of that risk, stopping threats at the gateway before they execute, and maintaining a complete audit record of every agent action.

AI agent detection and governance

SecureAI reads agent, model, and MCP server inventory directly from connected platforms without manual input, so every agent appears in the inventory regardless of how it was created. The dashboard surfaces agent counts by platform, unmanaged and high-privilege agent counts, the Enterprise AI Risk Score, and estate-wide guardrail coverage at a glance. Supported platforms for full governance: Microsoft Foundry, Microsoft Copilot Studio, Google Gemini Enterprise Agent Platform, and Zoho Zia. AI Risk Assessment additionally covers Anthropic Claude, GitHub Copilot, and Gemini.

Security detection signals

• Unmanaged agents: Agents with no named owner are flagged as a persistent risk condition and tracked until ownership is resolved, because an agent without a named responsible party has no accountability structure if it is compromised.
• High-privilege agents: Agents carrying elevated or sensitive permissions, identified across all connected platforms in a single view.
• Agents without guardrails: Counted separately as a headline metric on the dashboard, giving an immediate read on which agents have no behavioral constraints configured.
• Recently created and modified agents: Surface as they appear within connected platforms without a manual audit cycle.
• Memory Enabled Agents (Gemini Enterprise Agent Platform): Agents with persistent memory active across sessions carry state that could be influenced by prior interactions, identified separately as a distinct risk signal.

AI agent security assessment

While detection identifies which agents carry risk, securing AI agents completely requires understanding the scope of that risk across the estate — the posture read needed to prioritize response before an incident forces the issue.

Enterprise AI risk score

The Enterprise AI risk score evaluates the tenant configurations of enterprise AI platforms including Anthropic Claude, GitHub Copilot, and Gemini for security misconfigurations, and aggregates platform-level security posture into a single metric visible on the dashboard.

Agent access graph

For any individual agent, the Agent Access Graph draws together its roles, permissions, capabilities, model, MCP services, and the tools those services expose into a single view, making the full scope of an agent's access visible without navigating across multiple platform consoles. The graph helps evaluate the potential impact of a misconfigured or compromised agent.

Platform-level security posture signals

• Guardrail coverage: Tracked as an aggregate ratio across AI Foundry, Google Gemini Enterprise Agent Platform, and Zia, with agents that have no guardrail configured surfacing as a separate dashboard count.
• Agent IAM Roles, Denied Policies, Agent Identities, and Service Accounts (Gemini Enterprise Agent Platform): Four distinct permission data types drawn into a single view, giving a complete picture of the trust granted to each GeminiVertex agent.
• Agent Accessibility Report (Gemini Enterprise Agent Platform): Shows the resources and services each Gemini agent can reach, informing access control decisions at the individual agent level.
• Blocked Agents (Microsoft): Surfaces agents blocked at the Microsoft platform level, a risk signal relevant to certification scope even when gateway enforcement is not in play.

Stop threats at the gateway

Agentic AI security requires a control point that acts before damage occurs rather than detecting it afterward, which is why SecureAI's MCP gateway sits in the request path between every agent and every MCP server it connects to. Because agents reach the gateway URL rather than the backend server directly, every tool call is evaluated against policy before it reaches the backend, and a blocking decision applied at the gateway has no alternative path around it for agents subject to Access Protection.

Least-privilege enforcement at the tool call level

At MCP server registration, each tool the server exposes is classified as a read or write operation, which is the basis for per-tool enforcement: an agent can be permitted to read from a server while write and delete operations on the same server remain blocked by policy, rather than having access granted or revoked at the server level alone.

Context-aware control policy

Control Policy evaluates every gateway request against agent identity, tool name, IP address category, time of day, user attributes, MFA status, and the agent's own risk score. Two policy types apply in the same transaction on every call: Access Policy, which allows or blocks the request based on whether the agent is permitted to reach a server or invoke a specific tool; and DLP Policy, which inspects request and response content and either redacts matched sensitive content before the request continues or blocks the request entirely.

Prebuilt threat policy library

SecureAI ships with 48 prebuilt policies mapped to OWASP Top 10 for AI and 64 mapped to MITRE ATLAS, each named for the specific threat it addresses and ready to enable on any registered gateway from installation, giving security teams a threat-mapped enforcement baseline without custom policy authoring. Examples include Block-Agent-Tool-Poisoning (ATLAS AML.T0110), Block-Dark-Patterns-Outbound (OWASP AS106), Block-Inference-API-Exfiltration (ATLAS AML.T0024), AfterHours-Destructive-Plus-Injection, and Block-Geo-Restricted-Access.

Data leakage prevention policies

The data leakage prevention policy covers both the content an agent sends to a backend server and the response content that comes back. The built-in pattern library contains 117 data patterns filterable by country and compliance framework, covering GDPR, PIPEDA, PHIPA, PCI DSS, SOX, LGPD, FCRA, EFTA, PIPL, and others, with custom regex patterns available for organization-specific data types. When a data leakage prevention policy is applied, the gateway either redacts the matched content or blocks the request in full, configurable per policy.

Time-based and IP-based enforcement

The prebuilt Business Hours parameter defines weekdays 09:00 to 18:00 and serves as the negation condition for the after-hours policies in the library, so destructive write operations, delete operations, and injection-pattern tool calls are automatically blocked after hours and on weekends. IP address category parameters apply independently of the time condition, so requests from flagged network origins are blocked regardless of when they arrive.

Complete AI agent security audit record

All monitoring and reporting in SecureAI is the product's own capability generated across the MCP gateway layer, not pulled from native platform consoles, producing a consistent cross-platform audit record. Every report and log is read-only and provides the evidence base for security investigations and governance decisions.

Gateway activity

Gateway Activity logs every gateway request with fields including Timestamp, Agent Principal Name, Tool Name, Policy Action, Policies Matched, Status, Location, and Source IP, where Policies Matched identifies which specific control policies fired and Source IP alongside Location shows where each request originated.

Insight reports

• Agent Principal Usage Report: Activity volume and patterns per agent identity across the gateway.
• MCP Tools Usage Report: Which tools are called, by which agents, how often, filterable by blocked vs. allowed.
• MCP Servers Latency Report: Response times per MCP server, surfacing degradation that could indicate gateway misconfigurations or unusual traffic patterns worth investigating.
• Inactive MCP Servers: Servers with no recent traffic, a signal for attack surface review.
• MCP Server Error Report: Error rates and failures per server.
• Control Policy Audit: Auditable trail of every allow, block, and redact decision at the gateway.

Platform-level security reporting

Per-platform reports surface agent configurations that gateway logs alone cannot capture. Microsoft reports include Blocked Agents and All Guardrails. Google Gemini Enterprise Agent Platform reports cover Memory Enabled Agents, Agent IAM Roles, Agent Denied Policies, All Agent Identities, All Service Accounts, and the Agent Accessibility Report. Zia reports cover Agent Versions and capability reports for tools, models, and knowledge base documents.

Close the entitlement risk that gateway enforcement cannot reach

Gateway enforcement stops unauthorized tool calls at the point of execution, but it does not address the risk created by agents that have accumulated entitlements they should not hold, access that was granted at some point and has never been reviewed or revoked.

Access Certification Campaigns

Campaigns run on a defined schedule, with reviewers receiving structured approve-or-revoke decisions for existing agent access routed automatically to the correct owner by Governance Assigning Rules. Each campaign produces a documented decision record, and entitlements revoked through a campaign reduce the access an agent carries into its next session, limiting potential impact going forward rather than only at individual access events.

Why access certification matters for AI agent security

Most AI agent security tools treat entitlement risk as a visibility problem, surfacing which agents have excessive access without providing a structured mechanism to act on that signal. SecureAI provides a repeatable, documented review cycle that produces a named decision for every entitlement in scope, so an agent passing a certification campaign carries only the access a named reviewer confirmed it should have.

Why security teams choose SecureAI for AI agent security

• Enforcement before execution: The MCP gateway evaluates identity, time, IP, MFA status, and agent risk score on every tool call before the backend sees the request, so policy decisions exist as live controls rather than documented intentions.

• Threat-mapped baseline from day one: 112 prebuilt policies mapped to OWASP Top 10 for AI and MITRE ATLAS are available from installation, so security teams are not starting from an empty policy set when they onboard their first gateway.

• Entitlements resolved, not just surfaced: Access Certification Campaigns produce approve-or-revoke decisions on a defined schedule, reducing the access agents carry forward rather than only identifying which agents have too much.

• Data that stays in your environment: An on-premises deployment helps organizations retain agent inventory, policy decisions, and gateway logs within their infrastructure. This can support data-location and compliance requirements when combined with appropriate governance, legal, and technical controls.