Bedrock Knowledge, the fashionable DSPM platform supplier for data-centric safety, governance and administration, introduced native help for Atlassian Confluence, bringing information safety visibility into unstructured SaaS collaboration environments that more and more function each repositories for delicate enterprise data and inputs for AI programs. Fairly than merely discovering what delicate information exists in Confluence, the mixing maps how that information flows into AI programs and what data fashions might probably expose throughout inference.
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Safety groups face a brand new problem as SaaS adoption continues to develop concurrently AI adoption is accelerating: loads of their most delicate information now lives outdoors conventional databases, embedded in collaboration platforms like Confluence. When this unstructured information feeds AI programs, together with retrieval-augmented technology (RAG) implementations utilizing companies like AWS Bedrock Data Bases, organizations lose visibility into what data their AI fashions can entry and floor. This leaves groups with no clear understanding of what delicate information exists the place, who can entry it and the way it’s finally used.
“Confluence is without doubt one of the most requested SaaS information sources as a result of, for a lot of organizations, it holds huge quantities of inside data, together with commerce secrets and techniques, mental property and buyer data,” mentioned Bruno Kurtic, co-founder, President, and CEO of Bedrock Knowledge. “This content material is more and more used throughout analytics, collaboration and AI use circumstances together with as a foundation for RAGs. Organizations want to make sure it’s correctly categorised, protected and compliant with inside governance requirements and regulatory necessities, with clear visibility into how delicate data is accessed and used.”
Understanding AI Inference Danger by Mapping Knowledge to Fashions
Whereas some safety instruments supply partial protection of SaaS or AI coaching information, Bedrock Knowledge connects SaaS discovery, delicate information classification, and AI inference lineage in a single DSPM platform. What differentiates Bedrock Knowledge’s strategy is its means to map delicate information again to the AI fashions utilizing it for inference, permitting safety groups to know not simply the place delicate information lives, however what an AI mannequin might probably floor in response to a question and whether or not that information may very well be uncovered to an unauthorized consumer.
The brand new integration delivers:
- Complete Confluence Discovery: Mechanically discovers all Confluence areas and maps content material throughout folders, pages, reside pages and blogs Advanced.
- Permission Decision: Analyzes permissions at each degree and resolves inherited and oblique entry paths, revealing efficient entry typically obscured by space-level or parent-object permissions.
- AI-Pushed Classification: Scans unstructured textual content to determine PII, secrets and techniques and mental property, indexing all metadata within the Bedrock Metadata Lake
- Unified Danger Querying: Safety groups can question Confluence threat alongside different SaaS, cloud and AI information sources via a single platform
- Least-Privilege Safety Mannequin: Requires solely a read-only, fine-grained Atlassian entry token, guaranteeing discovery and evaluation with out introducing write permissions or operational threat.
The Bedrock Knowledge integration mechanically discovers all Confluence areas and maps content material throughout folders, pages, reside pages and blogs, analyzing permissions at each degree and resolving advanced inherited and oblique entry paths. It reveals efficient entry that’s typically obscured by space-level or parent-object permissions.
With native Confluence help, Bedrock Knowledge continues to broaden DSPM past conventional information shops into the SaaS instruments and AI workflows the place enterprise threat more and more resides, serving to safety groups shut visibility gaps earlier than they end in information publicity.
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