/solutions

Built for Every Stakeholder
in the AI Decision Chain

Atlas addresses specific operational challenges across regulated AI environments. From the boardroom to the data center, every role gets the controls and visibility they need.

By Role

Atlas for Your Role

Different stakeholders face different pressures when AI meets enterprise data. Atlas gives each role the specific controls they need.

Every new AI tool is a potential data exfiltration vector. Shadow AI is already in production. You need governance that scales with the business, not a blanket ban that gets ignored.

Policy-enforced data classification prevents sensitive content from reaching unauthorized models
Immutable audit logs capture every query, retrieval, and model interaction for forensic review
Zero-trust boundaries isolate AI workloads from production data stores
Anomaly detection flags unusual access patterns before they become incidents
Compliance dashboards map controls directly to SOC 2, ISO 27001, and NIST frameworks

Engineering teams want to move fast with LLMs and RAG pipelines, but every deployment raises questions about data residency, vendor lock-in, and operational risk. You need a platform that gives teams velocity while keeping the architecture sound.

Deploy on your infrastructure with support for air-gapped, hybrid, and multi-cloud topologies
Swap models, vector stores, and embedding providers without rewriting application code
Policy-as-code integrates with existing CI/CD pipelines and GitOps workflows
Horizontal scaling with configurable rate limiting and priority lanes per tenant
Full API compatibility with OpenAI, Anthropic, and open-source model endpoints

HR sits on some of the most sensitive data in the organization: compensation, performance reviews, medical accommodations, disciplinary records. AI can transform how you operate, but one leak destroys trust.

Field-level access controls ensure AI models only see data appropriate to the requesting context
PII redaction strips personally identifiable information before it reaches model inference
Role-based query restrictions limit what questions can be asked of employee datasets
Anonymized analytics allow workforce planning insights without exposing individual records
Complete audit trails show exactly who queried what employee data and when

Data is spread across object stores, data lakes, legacy archives, and SaaS platforms. AI tools want access to everything. You need a governance layer that works across all of it without migrating data.

Metadata-driven classification tags data at rest before AI systems ever touch it
Connectors for S3, Azure Blob, GCS, NFS, and on-premises storage systems
Tiered access policies control which datasets are available to which AI workloads
Storage-layer integration means no data duplication or migration required
Lineage tracking shows exactly which source documents contributed to every AI response

AI systems are consuming data faster than governance programs can catalog it. Data quality, lineage, and classification are lagging behind adoption, and every ungoverned dataset is a liability waiting to surface in an audit.

Automated classification tags every dataset before AI systems can access it
Data lineage tracking traces every AI output back to its source records
Quality gates block models from training or inferring on stale, incomplete, or unvalidated data
Centralized data catalog integrates with Atlas policies for unified governance visibility
Cross-domain access controls enforce data sharing agreements between business units

Risk registers were built for traditional IT threats, not probabilistic AI systems that hallucinate, drift, and operate on data they were never designed to see. You need risk frameworks that map to how AI actually works.

Risk scoring for every AI workflow based on data sensitivity, model type, and access scope
Continuous control monitoring validates that governance policies are enforced, not just written
Automated evidence collection for SOC 2, ISO 27001, NIST AI RMF, and EU AI Act requirements
Incident response integration triggers alerts when AI systems violate policy boundaries
Risk dashboards provide board-ready reporting on AI exposure across the organization

Every department wants AI, but there is no unified framework for evaluating, deploying, and governing AI initiatives. Without centralized oversight, the organization accumulates technical debt, compliance gaps, and reputational risk.

Organization-wide AI inventory catalogs every model, dataset, and pipeline in production
Standardized evaluation criteria for new AI initiatives with governance pre-checks
Usage analytics reveal adoption patterns, cost drivers, and underperforming deployments
Policy templates encode organizational AI principles into enforceable technical controls
Executive dashboards track AI program maturity against industry benchmarks and regulatory timelines

AI workloads behave nothing like traditional microservices. They have unpredictable resource demands, long-running inference calls, and data dependencies that break standard deployment patterns. You need operational tooling built for this reality.

Infrastructure-as-code deployment with Terraform and Helm chart support for Atlas components
Health monitoring and autoscaling tuned for AI inference and retrieval workload patterns
Secrets management integration with HashiCorp Vault, AWS KMS, and Azure Key Vault
Canary deployments and rollback for policy changes without disrupting live AI workloads
Observability pipelines export metrics and traces to Datadog, Grafana, and Splunk

Traditional audit methodologies do not cover AI-specific risks like training data bias, retrieval accuracy, or model drift. You need audit evidence that is machine-generated, tamper-evident, and tied to specific control objectives.

Tamper-evident audit logs with cryptographic integrity verification for every AI interaction
Pre-built audit programs mapped to COBIT, NIST, and ISO control frameworks for AI systems
Sampling tools for statistically valid review of AI decision outputs and data access patterns
Segregation of duties enforcement across model training, deployment, and data access roles
Exportable evidence packages formatted for external auditor consumption and regulatory submission
Capabilities

Core Solution Areas

01

Private Document Intelligence

PROBLEM

Sensitive documents exposed to AI systems without access controls.

SYSTEM

Governed retrieval pipelines with policy-based access and data classification.

OUTCOME

Internal AI that operates on documents without exposing raw sensitive content.

02

Governed Agents

PROBLEM

AI agents operating without boundaries, accessing arbitrary data and tools.

SYSTEM

Policy-constrained agent execution with tool gating and scope limits.

OUTCOME

Autonomous operations that respect organizational governance.

03

Observability Retrofit

PROBLEM

Existing AI deployments with no visibility into data access or model behavior.

SYSTEM

Audit logging, query tracing, and retrieval tracking layered into existing pipelines.

OUTCOME

Complete auditability without rebuilding the AI stack.

04

Scientific Data Inference

PROBLEM

Large, sensitive research datasets archived and inaccessible to AI.

SYSTEM

Metadata-driven inference on structured representations of archived data.

OUTCOME

Run AI on data from sequencers, instruments, and archives without rehydration.

By Industry

Where Atlas Operates

Regulated industries share a common problem: AI adoption blocked by data governance gaps. Atlas bridges that gap with infrastructure-level controls.

Talk to us about
your environment

Every deployment is different. We scope Atlas to your infrastructure, your data, and your compliance requirements.

Contact Us