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Collaborating

​Governance & Ethics

The Strategic Governance Partnership Model

 

KARA Advisors provides AI governance advisory services through a specialized partnership model, enabling organizations to implement ethical AI systems while maintaining focus on core business objectives. Our approach combines expert assessment capabilities with a curated network of governance specialists, ensuring compliance without operational burden.

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WELCOME

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AI Governance Pillars

Governance Readiness Assessment

AI Maturity Evaluation: 

  • Analysis of existing AI systems against NIST AI RMF and ISO 42001 standards

  • Risk Gap Identification: 35+ parameter audit covering algorithmic bias, data provenance, and regulatory alignment

  • Partner Analysis: Recommendations from pre-vetted governance specialists

Governance Architecture Design

Control Framework Development:
     A)  KARA Assessment -->   
     B)  Board Engagement --> 
     C)  Policy Automation  -->  
     D)  Partner Integration -->       
     E )  Compliance SLAs    --> 
     F)  Real-Time Monitoring --> 
     G)  Specialist Handoff

Key Compliance Standards Supported

  • NIST AI RMF

  • ISO 42001

  • EU AI Act

  • CCPA/GDPR

  • Sector-specific regulations

Continuous Program Management

  • Ensure executive leadership's active involvement in AI Governance process integration

  • Address under-investment in AI Governance execution management (reported by 91.2% of business owners)

 

Value Proposition:

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  • Risk Reduction: 85% faster compliance implementation vs. in-house efforts

  • Cost Efficiency: 40-60% lower governance overhead through shared partner resources

  • Strategic Alignment: Continuous monitoring of 150+ global AI regulations

Implementation Workflow:

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  1. Discovery Phase (2-4 Weeks)

    • Process mapping of 15+ AI governance components

    • Stakeholder interviews with C-suite/Board members

  2. Solution Design (3-6 Weeks)

    • Customized control frameworks aligned with:

      • EU AI Act requirements

      • CCPA/GDPR compliance thresholds

      • Industry-specific regulations

  3. Partner Transfer (1-2 Weeks)

    • Secure knowledge transfer via encrypted portals

    • SLA negotiation with 10-30% revenue sharing models

By integrating these practices, organizations can build AI governance frameworks that prioritize ethics, foster trust, and ensure responsible AI development and deployment

Vendor-Agnostic Tool Selection

1

     Implementation roadmaps for:

  • Compliance automation platforms

  • Bias detection engines

  • Audit trail systems

Technical and Data Readiness

2

  • Data Readiness: Assessing data quality, accessibility, and governance for effective AI deployment

  • Technology Infrastructure: Analyzing current technology setups to support AI initiatives

Organizational Preparedness

3

  • Workforce Capabilities: Evaluating existing skills related to AI and identifying skill gaps

  • Cultural Readiness: Assessing organizational mindset and values that support AI integration

  • Operational Processes: Identifying areas where AI can enhance efficiency

 

Risk and Implementation Planning

4

  • Risk Assessment: Identifying potential risks and developing mitigation strategies

  • Investment Planning: Creating a roadmap for implementation with defined milestones and expected benefits

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AI Readiness Framework: Enabling Responsible Integration

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1.    Establish clear ethical guidelines and embed them into AI design and development processes from the beginning
2.    Create a culture of ethical awareness through regular training and education on AI ethics for all stakeholders
3.    Implement robust risk management frameworks, including thorough impact assessments to understand potential consequences of AI applications
4.    Engage diverse stakeholders in the governance process to consider multiple perspectives and identify ethical issues early
5.    Conduct regular ethical risk assessments before deploying AI applications
6.    Establish transparent and accountable mechanisms for monitoring and evaluating AI systems, including regular audits and reviews

7.    Implement fairness measures and bias mitigation techniques, such as sourcing diverse training data and performing algorithmic audits
8.    Ensure human oversight by involving skilled decision-makers to supervise and interpret AI outputs
9.    Create dedicated roles like AI ethics officers and data stewards to oversee ethical compliance
10.    Establish an ethical oversight board to ensure adherence to ethical guidelines and industry standards
11.    Promote transparency by clearly communicating how AI systems operate, including data handling and decision-making processes
12.    Implement comprehensive data governance policies to ensure ethical and responsible data management

Ethical considerations

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