Ravula AI

AI Compliance & Governance

Build AI usage policies, risk frameworks, and evaluation metrics. Operationalize compliance for HIPAA, FINRA, FDA, and other regulated sectors.

  • Reduce audit findings and AI risk exposure
  • Establish governance frameworks tailored to your industry
  • Deploy evaluation harnesses and audit trails
  • Create policies that scale with your AI initiatives

Who this is for

Compliance, Risk, Legal, and IT leaders in regulated industries who need to deploy AI safely and maintain regulatory compliance. Essential for healthcare, finance, government, and manufacturing companies subject to HIPAA, FINRA, FDA, SEC, or other regulations.

Typical titles:

  • • Chief Compliance Officer / VP Compliance
  • • Chief Risk Officer / VP Risk Management
  • • General Counsel / Chief Legal Officer
  • • Chief Information Security Officer / VP IT Security
  • • Chief Technology Officer / VP IT (in regulated sectors)

Trigger phrases you might be saying

  • ""We need AI governance policies but don't know where to start."
  • ""Our auditors are asking about AI risk management—we have nothing documented."
  • ""We're in healthcare/finance—how do we use AI and stay HIPAA/FINRA compliant?"
  • ""Teams are deploying AI tools without oversight—need centralized governance."
  • ""We need to evaluate AI systems for bias, accuracy, and safety before deployment."
  • ""Regulators are asking about our AI usage—need audit-ready documentation."

Business outcomes

Audit readiness

Zero findings

Comprehensive documentation and controls for regulatory audits

Risk reduction

60–80%

Lower AI risk exposure through systematic governance and evaluation

Policy coverage

100%

All AI use cases covered by governance framework and policies

Deployment confidence

Validated

AI systems evaluated and approved before production deployment

What we deliver

  • AI governance framework

    Policies, procedures, and organizational structure for AI oversight

  • Risk register & assessment templates

    Systematic risk identification, scoring, and mitigation planning

  • Evaluation harness & testing protocols

    Automated testing for accuracy, bias, safety, and compliance

  • Audit trail & logging infrastructure

    Comprehensive logging of AI decisions, inputs, and outputs for compliance

  • Industry-specific compliance documentation

    HIPAA, FINRA, FDA, SEC, or other regulatory alignment documentation

  • Training & adoption materials

    Policies, procedures, and training for your team

Regulated sector variants

Healthcare (HIPAA)

Patient data protection, PHI handling, clinical decision support validation, and medical device AI compliance.

Focus: Data privacy, clinical validation, audit trails for patient care decisions

Finance (FINRA/SEC)

Trading algorithm oversight, customer data protection, KYC/AML compliance, and financial advice AI governance.

Focus: Algorithmic trading controls, customer data security, regulatory reporting

Manufacturing (FDA/GMP)

Quality control AI validation, production process oversight, documentation requirements, and traceability for regulated products.

Focus: Quality validation, process documentation, change control

Government (FedRAMP/State)

Citizen data protection, procurement compliance, algorithmic transparency, and public sector AI ethics requirements.

Focus: Data sovereignty, transparency, public accountability

How it works

Step 1

Assess

Inventory current AI use cases, identify regulatory requirements, assess risk exposure, and document existing controls. We interview stakeholders and review systems.

Step 2

Design

Create governance framework, policies, risk register, and evaluation protocols. We tailor frameworks to your industry and regulatory requirements.

Step 3

Deploy

Implement evaluation harnesses, audit trails, and monitoring. Train your team, establish approval workflows, and validate compliance.

Timeline & effort

Duration

4–8 weeks

Depending on scope, industry complexity, and number of use cases

Your team's time

12–20 hours

Total stakeholder time for interviews, reviews, and training

Timeline factors:

  • • Basic framework (single industry, few use cases): 4–5 weeks
  • • Standard framework (multiple use cases, moderate complexity): 6–7 weeks
  • • Complex framework (multi-industry, many use cases, custom requirements): 7–8 weeks

Pricing bands

$15,000–$40,000

Initial framework setup + optional monthly retainer for ongoing governance

Pricing factors:

  • Basic framework (single industry, 1–3 use cases): $15–25K
  • Standard framework (multiple use cases, moderate complexity): $25–35K
  • Complex framework (multi-industry, many use cases, custom requirements): $35–40K
  • Ongoing retainer (governance support, policy updates, audit prep): $3–8K/month
  • Add-ons: Additional use case evaluations ($2–5K each), custom compliance documentation ($5–10K)

KPIs we move

Governance frameworks improve risk management and compliance metrics across Universal Chart of Accounts processes:

Audit findings

Compliance violations

Risk exposure score

Policy coverage %

AI system approval time

Incident response time

Data breach risk

Regulatory penalty risk

AI accuracy rates

Bias detection scores

Audit trail completeness

Policy adoption rate

Tech stack & integrations

We deploy evaluation harnesses, audit logging, and monitoring tools that integrate with your existing AI systems and compliance infrastructure.

Evaluation & monitoring:

  • • Custom evaluation harnesses (Python/TypeScript)
  • • Bias detection frameworks (Fairlearn, Aequitas)
  • • Model monitoring platforms (MLflow, Weights & Biases)
  • • Audit logging systems (ELK stack, Splunk, custom)

Compliance integrations:

  • • GRC platforms (ServiceNow, MetricStream, Archer)
  • • Document management (SharePoint, Confluence, custom)
  • • Identity & access management (Okta, Azure AD, AWS IAM)
  • • Security information systems (SIEM, DLP, encryption tools)

Risks & safeguards

Regulatory non-compliance and penalties

Risk: AI systems violate HIPAA, FINRA, FDA, or other regulations, leading to fines, legal action, or business restrictions.

Safeguard: Industry-specific compliance frameworks, regular audits, evaluation protocols that test for regulatory alignment, and documentation that demonstrates due diligence to regulators.

AI bias and discrimination

Risk: AI systems make biased decisions that discriminate against protected classes, leading to legal liability and reputational damage.

Safeguard: Bias testing frameworks, diverse training data validation, fairness metrics monitoring, and human oversight for high-stakes decisions.

Data breaches and privacy violations

Risk: Sensitive data exposed through AI systems, violating privacy regulations and causing customer trust issues.

Safeguard: Data encryption, access controls, audit logging, data minimization practices, and privacy impact assessments before AI deployment.

Governance framework not adopted

Risk: Policies created but not followed, leading to shadow AI deployment and compliance gaps.

Safeguard: Change management, training programs, approval workflows, monitoring for unauthorized AI use, and regular policy reviews with stakeholders.

Caselets

Regional Healthcare System

Challenge: Deploying AI for clinical decision support but concerned about HIPAA compliance and medical liability. Auditors asking about AI governance.

Solution: Built HIPAA-aligned governance framework with PHI handling policies, clinical validation protocols, audit trails for patient care decisions, and bias testing for diagnostic AI systems.

Impact: Zero audit findings, approved AI deployment for 5 clinical use cases, reduced legal risk, improved patient safety through systematic validation.

Mid-Market Financial Services Firm

Challenge: Using AI for customer service and fraud detection but need FINRA/SEC compliance. Teams deploying AI tools without oversight.

Solution: Created FINRA-aligned governance framework with algorithmic trading controls, customer data protection policies, KYC/AML compliance validation, and centralized approval workflows for all AI deployments.

Impact: 100% policy coverage, zero regulatory violations, faster AI approval process (2 weeks vs. 6 weeks), reduced risk exposure by 70%.

Frequently asked questions

How do you ensure compliance with specific regulations (HIPAA, FINRA, FDA)?

We have industry-specific frameworks that map to regulatory requirements. We review your specific regulations, identify AI-related compliance obligations, and design policies and controls that demonstrate due diligence. We also provide documentation templates that align with audit requirements.

What if we already have some AI systems deployed?

We assess existing systems and bring them into the governance framework. This includes evaluating current systems for compliance gaps, documenting existing controls, and creating remediation plans where needed. We can also grandfather certain systems with appropriate risk mitigation.

How do evaluation harnesses work?

Evaluation harnesses are automated testing frameworks that validate AI systems before deployment. They test for accuracy, bias, safety, and compliance using test datasets and defined metrics. We build custom harnesses for your specific use cases and integrate them into your CI/CD pipeline or approval workflows.

What's included in the ongoing retainer?

The retainer ($3–8K/month) includes: policy updates as regulations change, evaluation of new AI use cases, audit preparation support, quarterly risk assessments, training for new team members, and access to our compliance experts for questions. It ensures your governance framework stays current and effective.

How long does it take to get AI systems approved under the framework?

With a well-designed framework, approval typically takes 1–2 weeks for standard use cases. Complex or high-risk use cases may take 3–4 weeks. The framework includes streamlined approval workflows, pre-approved templates for common use cases, and clear risk thresholds that determine approval level required.

Can this work for companies not in heavily regulated industries?

Yes. Even non-regulated companies benefit from AI governance to manage risk, ensure quality, and scale AI responsibly. We can create lighter-weight frameworks focused on risk management, quality assurance, and ethical AI use rather than regulatory compliance.

What happens if we have an AI incident after deploying the framework?

The framework includes incident response procedures. We help you investigate, document, remediate, and learn from incidents. The audit trail and evaluation data help identify root causes. We also update policies and controls based on lessons learned to prevent similar incidents.

Ready to build your AI governance framework?

Book a 20-minute fit call to discuss your compliance requirements and see if our governance framework is right for your organization.

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Last updated: November 2025