Governance that makes AI scale
Turn policy into practice with a control system built into day-to-day work, so value moves fast and stays safe.
AI only scales when leaders can trust it. We design a practical control system that satisfies risk, audit, legal, and security while keeping delivery fast. Governance lives inside tools and workflows, not in shelfware policies.
Why Governance First
What changes for you
Clear risk tiers and approval paths
Evidence collected automatically as work happens
Consistent human oversight for material decisions
Faster audits, fewer surprises, easier scale
Core Framework We Implement
Risk tiering: classify use cases by impact on customers, employees, and the firm; set guardrails, approvals, and oversight per tier
Policy to practice: convert policy into checks inside tools, prompts, pipelines, and deployment steps
Evaluation harness: measure quality, cost, and latency, with test sets and thresholds per use case
Human in the loop: define decision points, escalation, and sampling plans
Evidence and traceability: maintain decision logs, datasets used, model configs, and change history
Incident management: monitor, alert, and respond with playbooks for quality drift, bias, and security events
Lifecycle controls: govern from idea to retirement, including change requests and periodic reviews
Key Artifacts You Receive
Responsible AI charter and decision principles
Risk tiering rubric and approval matrix
Controls library mapped to tiers and stages
Data and privacy checklist including DPIA templates
Model and use case cards with an AI bill of materials
Evaluation plan with metrics, test sets, and thresholds
Evidence pack: logs, audit trail, and review records
RACI for roles across product, risk, security, and operations
Roles and Responsibilities
Executive sponsor: sets intent and removes blockers
Risk and compliance define control points and reviews evidence
Security and privacy ensure access, logging, and data handling
Product and delivery: implement controls in workflow and tools
Data and platform: manage datasets, lineage, and environments
Independent reviewers: periodic assessments and red team exercises
Engagement Flow
Current state and risk map, one to two weeks
Assess policies, platforms, data, and in-flight use cases. Identify gaps, risks, and quick wins.
1
Control design, two to three weeks
Define tiers, approvals, evidence requirements, and human oversight. Map controls the delivery flow and platforms. Draft the controls library and templates.
2
Tool enablement, two to four weeks
Instrument evaluation, access, logging, and audit trail. Add checks to pipelines, prompts, and deployment steps. Stand up dashboards.
3
Validate and operationalize ongoing
Pilot on one or two use cases. Train reviewers and owners. Establish cadence for reviews, drift monitoring, and incidents. Hand off to internal owners.
4
Controls We Commonly Implement
Access controls and least privilege
Dataset registry and lineage tracking
Prompt and template versioning
Test sets with quality, cost, and latency thresholds
Bias and harm screens tailored to the use case
Decision capture with reason codes and samples
Change control with rollback and approvals
Monitoring for drift, anomalies, and abuse
Metrics We Track
Control coverage by tier and stage
Time to approve and time to deploy
Issues found in review versus in production
Evaluation pass rate against thresholds
Audit readiness and time to provide evidence
Alignment to Standards
We align to widely used practices such as the NIST AI Risk Management Framework, ISO guidance for AI risk and information security, SOC reporting expectations, and internal model risk frameworks. We adapt to your enterprise standards and regulators.
FAQs
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No. Controls are right sized by risk tier and embedded where work happens, so delivery remains fast and auditable.
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Yes. We integrate with your current platforms and processes.
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No. We design for current state and evolve with you.
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We bake data handling and approvals into the workflow and provide DPIA templates you can adapt.