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Katalyst Street Launches AccessMax Beta, Bringing Zero-Trust Security to Enterprise RAG
TelAve News/10909527
Identity-aware retrieval and vector ACL middleware, the first release in the company's AI Security Suite, keeps enterprise AI assistants from surfacing data users are not authorized to see.
ATLANTA - TelAve -- Katalyst Street announced the beta launch of AccessMax, an identity-aware search and RAG middleware proxy that enforces real-time access controls across enterprise AI applications. It sits between AI agents and vector stores, resolves employee identities across SaaS platforms, and blocks unauthorized data before a prompt is assembled.
The problem: Vector databases optimize for semantic similarity, not permissions. Source-system ACLs are often lost or flattened during ingestion, so AI assistants can surface restricted material, such as executive compensation, M&A terms, or private Slack channels, to the wrong employees.
The solution: AccessMax is a centralized enforcement layer that separates permissions logic from application code. At query time it applies dual-tag vector pre-filtering, dynamic ReBAC evaluation, sub-document masking, and zero-knowledge denials, before any chunk is retrieved or sent to the language model. Rajesh Koppula, the CEO quote says security teams no longer have to choose between search performance and strict compliance.
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Key capabilities:
Availability: The beta supports private VPC deployment via Kubernetes Helm charts, containerized cloud instances, or an embedded Python SDK. A demo site and pilot program contacts are included.
FAQ highlights:
More on TelAve News
The problem: Vector databases optimize for semantic similarity, not permissions. Source-system ACLs are often lost or flattened during ingestion, so AI assistants can surface restricted material, such as executive compensation, M&A terms, or private Slack channels, to the wrong employees.
The solution: AccessMax is a centralized enforcement layer that separates permissions logic from application code. At query time it applies dual-tag vector pre-filtering, dynamic ReBAC evaluation, sub-document masking, and zero-knowledge denials, before any chunk is retrieved or sent to the language model. Rajesh Koppula, the CEO quote says security teams no longer have to choose between search performance and strict compliance.
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Key capabilities:
- Identity resolution: Maps vendor-specific IDs (Slack, GitHub, Jira, Google) to one corporate identity with pre-computed group memberships.
- Adaptive query planning and tiered sync: Picks a retrieval strategy based on permission selectivity, and applies permission changes in sub-millisecond time without re-embedding.
- Sub-document controls: Field-level masking (such as SSN and salary), section restrictions, summary permission intersection, and GraphRAG path trimming.
- Auditability: Tamper-evident SHA-256 hash chains, automated PII redaction, and indistinguishable denials.
Availability: The beta supports private VPC deployment via Kubernetes Helm charts, containerized cloud instances, or an embedded Python SDK. A demo site and pilot program contacts are included.
FAQ highlights:
- AccessMax is the first layer of the AI Security Suite, alongside ShieldMax (gateway and PII redaction), AgentMax (agent RBAC and approvals), and TraceMax (telemetry and SIEM).
- It supports ACL, RBAC, ABAC, and ReBAC models across SharePoint, Google Drive, Confluence, Salesforce, Slack, GitHub, and Jira.
- Explicit DENY always overrides ALLOW, and broken folder inheritance is handled by flattening at ingestion or by ReBAC path evaluation.
- It fails closed during identity provider or cache outages, restricting retrieval to public partitions at most.
- Proof traces and the audit ledger give full decision lineage.
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Source: Katalyst Street Inc
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