Mimecast Expands Incident Response Capabilities with Runtime Data Security for Artificial Intelligence and Human Risks

Mimecast Expands Incydr with Runtime Data Security for AI and Human Risk

Mimecast has enhanced its Incydr offering with new data security capabilities and a preview of the Agent Risk Center. These advancements deliver runtime data security through a unified approach to detect, govern, and remediate data exposure in real-time, regardless of whether it is driven by employees or agents acting on their behalf.

• Integration of Runtime Data Security

• The integration of runtime data security addresses the growing concern surrounding enterprise data loss, which is no longer solely attributed to human error.
• With the increasing adoption of artificial intelligence (AI) agents, a new attack surface has emerged, as these agents can access and share sensitive data through pathways that traditional security tools were never designed to monitor.

• New Capabilities within Enhanced Incydr Platform

• Unified human and agent visibility: A single view into data loss risk across employees and autonomous agents, spanning endpoints, cloud and SaaS applications, browser activity, commercial AI tools, MCP server connections, and user-developed agents.
• Shadow AI and unsanctioned agent detection: Purpose-built detection for unsanctioned AI usage, out-of-policy commercial agents, unauthorized MCP connections to production databases and critical SaaS platforms, and user-built agents operating on unapproved Large Language Model (LLM) providers or accessing production environments without security review.
• Adaptive risk scoring for people and AI agents: The Incydr risk engine now continuously scores both human users and AI agents based on behavioral anomalies, policy violations, high-risk data access, unsanctioned application usage, agent compliance posture, and exposure to critical systems and data sources.
• Granular data-to-agent access mapping: A view of which agents and tools access which categories of sensitive data, including customer PII, source code, financial records, internal communications, HR data, and infrastructure configurations, enabling security teams to understand and control the agent-to-data blast radius.
• Policy-driven governance: A comprehensive framework for classifying and enforcing policy across all AI tools, commercial agents, MCP servers, and user-developed agents, with sanctioned, unsanctioned, and uncategorized classifications, department-level enforcement, and AI acceptable use policy management.

Agent Risk Center

• Anomaly Detection Engine for Risky Agent Behavior

• Automatically surfaces high-risk patterns, unsanctioned tools with production database access, finance users connected to payment MCP servers, user-developed agents using non-sanctioned LLM providers, and executives with overly broad MCP configurations.

• Governance Scorecards

• Provides a continuous assessment of organizational posture across four dimensions: policy coverage, review currency, human-in-the-loop enforcement, and LLM compliance, giving CISOs a measure of their agentic governance maturity.

• Department-Level Risk Heatmaps

• Offers visual analytics showing risk distribution, department-level exposure, risk factor breakdowns, and trend patterns, enabling targeted intervention rather than
  
  
  

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