Data governance, security and management startup Bedrock Labs Inc. is deepening its integration with Snowflake Inc.’s AI Data Cloud after securing a strategic investment from the cloud data warehouse giant.

The new partnership, announced today, will see Bedrock’s artificial intelligence-driven data classification and governance capabilities integrated with Snowflake Horizon, which is a service within the AI Data Cloud that helps organizations manage their data, applications and AI models.

In addition, Bedrock announced a new ArgusAI integration with Snowflake’s Cortex AI, which uses AI agents to perform tasks such as text processing, summarization and predictive analytics on behalf of unskilled workers. It enables companies to do this with enhanced governance and reduced exposure.

Bedrock said the integrations are timely, because organizations are under pressure to bring AI into their data workflows. The problem is that 79% of companies struggle to classify sensitive data used in AI systems. To do this, they need exactly the kind of petabyte-scale data discovery, classification and entitlements analysis that Bedrock provides.

Mapping data sensitivity

Co-founder and Chief Executive Bruno Kurtic said he was especially keen to integrate with Snowflake, because it’s one of the most popular data platforms for modern enterprises. “Securing that data, across both traditional analytics workloads and emerging AI applications, is a foundational requirement for any enterprise AI strategy,” he said. “Snowflake’s investment affirms that data-centric governance is not a nice-to-have, it’s a prerequisite for deploying AI with confidence.”

Bedrock is integrating its proprietary Metadata Lake platform with Snowflake Horizon from today. Metadata Lake is a continuously updated graph knowledge base that maps every dimension of enterprise data according to its sensitivity, lineage, entitlements, access patterns and business context. With the integration, it can now map all of this directly to the Horizon platform. It serves as the single source of truth for data sensitivity and risk context, enabling AI agents to be used across the Snowflake platform.

Customers will benefit from continuous visibility into their Snowflake environments. Metadata Lake enables them to discover and classify all sensitive information stored within them automatically, including personally identifiable information and financial data. For each dataset, Bedrock will assign an Impact Score to its associated schemas and tables, based on how sensitive the files are. Customers can then implement appropriate security controls for each one.

Moreover, by mapping entitlements across users, accounts, roles and AI agents, Bedrock is able to identify exactly who can access what kinds of data are stored within Snowflake. It does this by using Snowflake’s native tagging feature to label information at the database, table and column level, based on its sensitivity. Access controls can then be implemented within the Snowflake environment, while the Horizon catalogue ensures everything remains up to date in real-time.

Data access controls for AI agents

The second thrust of Bedrock’s integration will see ArgusAI combined with Snowflake’s Cortex AI, enabling Cortex agents to be inventoried and cataloged, so companies can map the data they’re allowed to access through Cortex Search and Cortex Analyst. Bedrock intends to showcase this integration at the upcoming RSAC cybersecurity conference in San Francisco next week, with availability shortly after.

Kurtic said this will be useful because traditional data security posture management tools were built before the era of AI agents. While they can discover sensitive data, they lack the ability to map the relationships, access paths and permissions of agents.

ArgusAI does all of this, enabling companies to create a unified exposure map that allows them to understand and contain the risks associated with agentic systems. It into Bedrock’s Data Bill of Materials, which is a continuously updated inventory of data assets linked to AI systems.

Organizations can then discover Snowflake Cortex Search services and identify the datasets indexed by it, gaining visibility into which datasets can be accessed by AI search applications. It can also correlate Cortex with role-based access controls to dictate which AI agents and applications can indirectly access sensitive data via AI search tools. Then, teams can flag any entitlement gaps they discover, fix the access paths and ensure appropriate controls are in place.

“As enterprises operationalize AI, the risk is defined by what those systems have access to,” Kurtic said. “If you don’t know what data your agents can access, through which MCP servers and with which entitlements, you can’t govern them.”

Snowflake Ventures’ head Harsha Kapre said strong governance minimizes risk, which means it’s the key to AI adoption. “Bedrock Data’s integrations with Snowflake Horizon and Snowflake Cortex AI help joint customers accelerate AI initiatives while maintaining security and compliance,” he said.

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