The Governance Layer
for Enterprise AI.
Users, agents, and data sources — all governed through a single policy engine. Atlas controls who accesses what, classifies every document on ingest, and enforces compliance across your entire AI stack in real time.
Without governance, AI creates risk.
Deploying AI against sensitive data without a governance layer exposes organizations to risks that existing security tools were not built to address.
AI models query across entire datasets without respecting organizational access boundaries or data classification.
Regulated data flows through inference pipelines with no audit trail, no policy checks, and no proof of compliance.
Organizations cannot see what data models accessed, what was returned, or how inferences were constructed.
Cloud-hosted AI services require data to leave the organization, breaking sovereignty and custody requirements.
Atlas solves this by introducing a governed intelligence layer between models and enterprise data — controlling access, enforcing policy, and logging every interaction.
Atlas sits between your models and your data.
Instead of letting models access raw data directly, Atlas governs how information flows through the AI system.
Policy + Classification + AuditScans and labels datasets by compliance category at ingestion. Classification metadata follows data through the entire pipeline.
Orchestrates governed RAG pipelines that enforce access boundaries on every query. Denied collections are excluded before vector search executes.
Every data request is evaluated against organizational policies — user identity, data classification, and operation scope — before execution.
Emits immutable audit events at every stage: query receipt, retrieval, inference, and response delivery. Complete trail for compliance and forensics.
Data Classification Engine.
Atlas scans datasets at ingestion and identifies compliance signals across categories. Organizations understand where regulated data exists before AI systems interact with it.
Every document passes through classification before entering the governed data layer.
Ingest
Data enters the system.
Scan
Classification engine analyzes content.
Tag
Compliance labels attached as metadata.
Enforce
Policy engine uses tags at query time.
Classification results are stored as metadata and referenced by the policy engine during query evaluation. Every collection carries its compliance profile forward through the entire pipeline.
RAG Governance.
Atlas orchestrates retrieval pipelines and ensures models only access approved collections. Every query is scoped, evaluated, and logged before documents reach the model context.
Authenticated principal and associated credentials determine base access level. Token validated against configured IdP before any query executes.
Role-based policies restrict retrieval scope to authorized departments and teams. Group membership evaluated via IdP claims at query time.
Compliance labels on collections are evaluated against the requesting context. A user without PCI clearance cannot retrieve from PCI-tagged collections.
Queries are scoped to specific projects, limiting lateral data access across boundaries. No fallback to broader scope when project filtering is active.
Policy Engine.
Define declarative policies governing how AI interacts with enterprise data. Policies are evaluated at query time and enforced before any data is returned.
Principals and roles are bound to collection-level permissions. No implicit access.
Model invocations are restricted to pre-approved data scopes per zone.
Processing rules enforce redaction, masking, or denial based on classification tags.
policy "restrict-pii-access" { scope = "collections:financial-records" enforce { require_role = ["compliance-officer", "senior-analyst"] require_project = true classification = ["PII", "PCI"] action = "allow-with-redaction" } audit { emit_event = true log_level = "detailed" retain_days = 2555 }}
Every interaction is recorded.
Atlas emits structured audit events at each stage of the pipeline, producing a complete trail of AI activity.
The original request, including principal identity, timestamp, and target collection scope.
Every document returned by the retrieval pipeline, with source collection and classification metadata.
The model endpoint called, token count, latency, and parameters used for inference.
The generated output, including any redactions applied and policy evaluations triggered.
Events are stored in append-only, tamper-evident logs. Queryable for compliance reporting, incident investigation, and operational analysis. Exportable as JSON-lines or CEF to external SIEM systems.
Infrastructure & Deployment.
Atlas integrates with modern AI infrastructure and deploys entirely within your environment. Policy enforcement applies at every integration boundary.
Deploy governed AI infrastructure.
Talk to the Aberspace engineering team about deploying Atlas in your environment.
