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.
Uncontrolled Data Access
AI models query across entire datasets without respecting organizational access boundaries or data classification.
Compliance Violations
Regulated data flows through inference pipelines with no audit trail, no policy checks, and no proof of compliance.
No Visibility
Organizations cannot see what data models accessed, what was returned, or how inferences were constructed.
External Data Exposure
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.
How Atlas Works
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.
Data Classification
Scans and labels datasets by compliance category at ingestion. Classification metadata follows data through the entire pipeline.
Retrieval Pipelines
Orchestrates governed RAG pipelines that enforce access boundaries on every query. Denied collections are excluded before vector search executes.
Policy Enforcement
Every data request is evaluated against organizational policies -- user identity, data classification, and operation scope -- before execution.
AI Observability
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.
Compliance Frameworks
Detected and enforced across all governed collections
Classification Pipeline
Every document passes through classification before entering the governed data layer.
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.
User Identity
01Authenticated principal and associated credentials determine base access level. Token validated against configured IdP before any query executes.
Organizational Roles
02Role-based policies restrict retrieval scope to authorized departments and teams. Group membership evaluated via IdP claims at query time.
Data Classification
03Compliance labels on collections are evaluated against the requesting context. A user without PCI clearance cannot retrieve from PCI-tagged collections.
Project Context
04Queries 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.
Policies Control
Evaluated on every request, no caching of decisions
Who can query specific collections
Principals and roles are bound to collection-level permissions. No implicit access.
What data models can access
Model invocations are restricted to pre-approved data scopes per zone.
How sensitive data is processed
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
}
}AI Observability
Every interaction is recorded. Atlas emits structured audit events at each stage of the pipeline, producing a complete trail of AI activity.
Query
The original request, including principal identity, timestamp, and target collection scope.
Retrieved Documents
Every document returned by the retrieval pipeline, with source collection and classification metadata.
Model Invocation
The model endpoint called, token count, latency, and parameters used for inference.
Response Generation
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.
Integrations
LLM Inference
vLLM, TGI, Triton, llama.cpp
Embedding Pipelines
Governed vectorization with metadata preservation
Vector Databases
Qdrant, Milvus, Weaviate, pgvector
Agent Frameworks
LangChain, LlamaIndex, custom agents
Storage Systems
S3-compatible, NFS, HDFS, Ceph
Identity Providers
LDAP, SAML, OIDC, Kerberos
Deployment Targets
On-Premises
Bare metal and VMware deployments with full hardware control
Private Cloud
OpenStack, private VPC, managed Kubernetes clusters
HPC Clusters
Slurm-managed GPU clusters and research compute grids
Air-Gapped
Fully disconnected environments with no external network path
Deploy governed AI infrastructure
Talk to the Aberspace engineering team about deploying Atlas in your environment.