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Bedrock Data has significantly expanded its ArgusAI platform to provide comprehensive governance over the enterprise AI risk surface. This update extends visibility and control across AI agents, Model Context Protocol (MCP) servers and connectors that enable data access, and the underlying enterprise data those systems can retrieve or expose. By mapping these interconnected components, ArgusAI helps organizations scale AI adoption safely while proactively containing risks from non-deterministic data access patterns and potential agent abuse.
As enterprises rapidly deploy AI agents and retrieval systems, traditional security and DSPM tools struggle to track dynamic access paths, shadow infrastructure, and unintended data exposure. ArgusAI delivers a unified exposure map that reveals what AI systems can actually reach, enabling security teams to govern risk end-to-end without stifling innovation.
ArgusAI, powered by Bedrock’s Metadata Lake, creates a continuously updated view of the AI footprint. At its foundation is the Data Bill of Materials (DBOM), an auditable inventory that catalogs every data asset connected to AI systems, including sensitivity classification, entitlement chains, regulatory context, and lineage. This transforms governance from guesswork into verifiable intelligence, allowing teams to understand and limit the blast radius of AI-driven access.
Model Context Protocol (MCP) servers serve as the connective layer between AI agents and enterprise systems, but misconfigurations, shadow instances, and over-permissive roles can create hidden exposure. ArgusAI’s MCP Server Discovery automatically identifies MCP endpoints across cloud environments, enriches them with data sensitivity and entitlement analysis, and maps full agent-to-MCP-to-role-to-data relationships. Prebuilt policies detect sensitive data exposure paths, while continuous monitoring tracks infrastructure and permission drift to surface emerging risks before they escalate.
Through a strategic relationship with Snowflake, ArgusAI now extends governance to Snowflake Cortex Search and Cortex Analyst. It automatically discovers managed Cortex services, identifies indexed datasets, correlates them with role-based access and underlying permissions, and flags overexposure or entitlement gaps. This ensures security teams maintain visibility into what enterprise data—especially regulated or customer-sensitive information—can be retrieved and returned via AI-powered search and RAG applications.
Bedrock Data’s new MCP server exposes authoritative data risk intelligence—categorization, classification, and exposure insights—from the Metadata Lake directly to enterprise AI workflows. This enables access reviews, incident response, remediation, and other automated processes to query trusted context in real time, embedding governance natively rather than retroactively. The result is consistent, scalable data awareness across AI operations without added overhead.
Bedrock Data ArgusAI empowers security and governance teams to keep pace with AI acceleration, providing the architectural context needed to make informed risk decisions, remediate exposure paths, and support responsible, high-velocity AI adoption across the enterprise.
About Bedrock Data
Bedrock Data delivers continuous, context-driven security and governance for enterprise data across private cloud, IaaS, PaaS, SaaS and AI environments. Powered by its patented Metadata Lake and Serverless Outpost architecture, Bedrock Data autonomously discovers, classifies and contextualizes data in place without moving it outside customer boundaries. Its open, API-first design integrates with existing platforms and enables natural-language policy enforcement, AI governance and automated remediation at enterprise scale. Global leaders in technology, finance, healthcare and biotech rely on Bedrock Data to make data security operational.
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