Overview
AI governance has a new legal standard: proof, not policy. If a regulator, board, litigant, or auditor asked your organization tomorrow to explain how an AI system made a decision, could you?
Most organizations have AI governance policies. Far fewer can reconstruct how their AI systems behaved, what data influenced an outcome, who approved deployment, or whether meaningful oversight existed at the moment a decision was made.
That gap is becoming one of the defining legal and operational risks of the AI era.
Data360 was built to close that gap.
We help organizations move from policy-based governance to evidence-based governance by embedding legal, technical, and operational controls directly into AI systems and workflows.
Policies promise governance. Pipelines prove it.
Who This is For
- General Counsel & Legal Teams
GCs are constantly navigating the gap between regulatory obligation and operational reality. We translate your legal requirements into verifiable technical controls — backed by engineering evidence.
- CISOs & Security Leaders
You will be held responsible for systems you cannot fully reconstruct. We build the audit infrastructure that gives you visibility — and a defensible position under scrutiny.
- Boards & Audit Committees
Carrying fiduciary responsibility for AI risk without line-of-sight to the stack. We design governance reporting that connects board oversight to technical operation.
- Regulated Industries & Consumer Platforms
Financial services, healthcare, media, and data-intensive enterprises where AI decisions carry high legal and reputational stakes — and where a governance gap is existential.
The Problem: Governance Fragility Meets AI Velocity
AI is exposing decades of infrastructure fragility and deferred decisions.
Organizations are being asked to govern systems that move faster than traditional controls and depend on fragmented environments they often cannot reconstruct under scrutiny.
The speed of AI deployment has outpaced institutional visibility.
Regulators, boards, courts, and counterparties are increasingly asking organizations to demonstrate:
- how systems behaved,
- what data influenced outcomes,
- who authorized deployment,
- and whether meaningful oversight ever existed.
Most organizations are not yet constructed to answer those questions consistently.
The Technology Translation Tax
Risk accumulates in the gap between legal requirements and technical operations.
Legal teams define obligations.
Engineering teams operate systems.
Critical governance information is often lost in translation between legal intent and system operation.
That is the translation problem Data360 was built to address.
Regulators Expect: Demonstrable governance, not declarations. The question is not whether you have a policy. It is whether your system produces evidence that your policy was followed.
Boards Expect: Accountability with line-of-sight to the technical stack. Oversight obligations are operationally defined. “We had a policy” is no longer a defense.
CISOs Are Asked: To manage and attest to systems they cannot fully reconstruct, across agentic AI, third-party pipelines, and dynamic data without built-in audit trails.
OPERATIONALIZED LAW
Built from combined legal, CISO, audit and incident response experience. We have sat on both sides of the table - and design from both perspectives simultaneously.
TECHNICAL TRUTH
We translate abstract governance requirements into hard systems, controls, and evidence artifacts - not advisor layered on top of technology. Governance built the stack.
LITIGATION-READY
We design architectures that stand up under regulatory and judicial scrutiny - the difference between a finding and a defense is a documented, verifiable evidence chain.
The Stack
This is the technical architecture required to produce defensible AI governance evidence.
- Foundational: Decision Surface Mapping
Determines outcomes, control paths, and operational risk by identifying where machine-learning and automated systems influence decisions across the enterprise.
- AI Discovery & ASPM
Security posture management across all AI assets — shadow AI, sanctioned systems, and third-party integrations.
- Agent Orchestration
Workflow control and identity governance for agentic AI — every automated action mapped to an accountable principal.
- Data Lineage & Pipeline Visibility
End-to-end provenance for training data, inference inputs, and outputs — the legal basis for every dataset, documented.
- Machine Identity Audit
AI agent actions tied to human accountability paths — the non-repudiation layer regulators demand and courts require.
- LLMOps & Model Lifecycle
Deployment pipelines with embedded governance checkpoints — change logs, validation gates, and rollback controls.
- Model Drift Monitoring
Behavioral baselines and alerting for performance degradation — a model that passed validation last year may not pass today.
- Shadow AI Detection
Discovery and remediation of unauthorized AI usage across the enterprise — ungoverned AI is uncontrolled risk.
- DSPM & Data Security
Data security posture management integrated into the AI governance layer — protecting the inputs that define model behavior.
Evidence Architecture: Five Artifacts Every Regulator Will Ask For
AI governance has become an evidence discipline. These are the five artifacts that demonstrate it.
Artifact 1: AI Model Inventory
Internal and third-party system ownership mapped. Data dependencies, access controls, and business purpose documented at the model level.
Artifact 2: Validation Records
Backtesting and stress testing logs. Complete change history with timestamps, approvers, and risk rationale at each decision point.
Artifact 3: Data Provenance
Legal basis and chain of custody for every training set — source, transformation, consent status, and retention posture, all documented.
Artifact 4: Performance Drift
Established baselines and behavioral alerting over time. Evidence that governance is ongoing — not a one-time snapshot at deployment.
Artifact 5: Machine Identity
AI agent actions tied to clear, human-understandable accountability paths. Identity Symmetry — every AI action mapped to a named principal.
Talk with Lowenstein’s Data360 Team About Your AI Governance Readiness
The question is not whether your organization has AI governance policies. The question is whether you can prove how your AI systems behave.
90-Day AI Platform Risk Assessment
A full system and inventory health check — shadow AI, model governance gaps, data lineage deficiencies, and machine identity exposure — with a prioritized remediation roadmap.
Control Mapping & Framework Alignment
Aligning NIST AI RMF, EU AI Act, and sector-specific guidance to operational evidence — turning compliance checklists into verifiable control architectures.
High-Risk Use Case Design
Engineering identity and governance infrastructure for agentic AI — where the stakes of a governance gap are highest and evidence requirements most demanding.
AI Asset Valuation & Restructuring
Evaluating the legal and operational integrity of AI systems for M&A or bankruptcy estates — including model ownership, data provenance, and governance liability exposure.
Shadow AI Discovery
A targeted audit to surface and govern unvetted AI agents across the enterprise — identifying the systems no one sanctioned and the risk no one documented.
Safety
Safety begins with visibility.
Privacy and cybersecurity are only part of the AI risk landscape. Machine-learning and automated systems can also create:
- discriminatory effects,
- unsafe operational outcomes,
- reputational harm,
- and legally significant downstream decisions.
Decision surface mapping helps organizations identify where those risks emerge across workflows, automation layers, and interconnected systems. These risks rarely emerge in isolation. They develop across interconnected systems, workflows, and decision pathways that require integrated legal, technical and operational oversight. Visibility is what allows organizations to move from reactive risk management to accountable, defensible AI governance.
