Skip to main content
Request a quote

Data risk and compliance engineering

Privacy, fraud/AML, and AI governance that work in production

Many compliance projects stop at documentation. We turn those requirements into practical controls in your data stack so risk rules are applied in day-to-day operations.

The gap between policy commitments and technical reality

Policies promise control, but systems still leak risk: privacy rules not applied in pipelines, unclear data flows, weak fraud checks, and AI models with little governance. We close that gap with controls you can actually operate.

What we deliver

Practical data risk controls from policy to production

Data risk audit

See your real exposure before regulators, clients, or incidents do

Technical audit of data flows, privacy controls, third-party transfers, and control effectiveness across your data platform.

  • Risk inventory across privacy, fraud/AML, and governance touchpoints
  • Evidence-based gap analysis: documented controls vs runtime behavior
  • Prioritized remediation roadmap with implementation sequencing
Request a data risk audit

Data privacy controls

Privacy rules applied in your data platform, not just in a document

We design and implement privacy controls that connect legal requirements to actual data handling: access rules, purpose limitation, and retention enforcement.

  • Privacy rules applied at the data layer, not only at the perimeter
  • Purpose-based access control and data classification
  • Verification that declared policies match actual data handling
Strengthen data privacy controls

Data governance and lineage

Know where data comes from, where it goes, and who has access

We map and operationalize data lineage across collection, transformation, storage, transfer, retention, and model usage.

  • Lineage and ownership inventory tied to real systems
  • Transfer and access controls aligned to legal basis and policy
  • Retention, deletion, and evidence-ready traceability
Map governance and lineage

Why us

What makes this different from generic compliance work

  • We implement controls, not just frameworks

    We translate policy requirements into data rules, system behavior, and operational evidence.

  • We work where data decisions happen

    From APIs and pipelines to models and reports, we validate controls at the points where data is actually processed.

  • We combine privacy, fraud/AML, and AI governance

    Most teams treat these domains separately. We design coherent controls across the full data lifecycle.

  • We ship working controls, not slide decks

    Our deliverables are operational: rules in pipelines, governance checks in workflows, and evidence you can show an auditor.

Who this is for

Data and analytics teams in regulated industries

You need technical controls that survive audits, not only policy templates.

Organizations facing privacy, fraud/AML, or AI compliance pressure

You need clear evidence of control effectiveness across data systems, models, and third-party dependencies.

Leaders modernizing risk and governance operations

You want governance integrated into your data architecture, with clear ownership, traceability, and measurable outcomes.

Tell us where risk is leaking in your data stack

Share your current context in a few lines: data platform, compliance pressure, and what is failing today. We will review it and come back with a concrete technical plan.