Ensure ethical, compliant, and responsible AI adoption
Tailored AI governance solutions that ensure compliance, mitigate risks, and drive innovation across your organisation.
A trusted leader in AI Governance and Risk Management
At Calimere Point, we are uniquely positioned to help organisations navigate the complexities of AI governance. Born as a risk management company, we have extensive experience delivering complex risk management solutions across a broad spectrum of challenges, from financial to non-financial and model risks. Our expertise allows us to approach AI governance not only as a compliance necessity but as a critical risk management exercise.
Why AI Governance Matters
The rise of AI, especially in high-risk areas like generative AI, requires proactive governance. Regulations like the EU’s AI Act and U.S. Executive Orders emphasize risk management for AI models, ensuring compliance while addressing ethical and safety concerns.
Empowering Responsible AI Adoption
AI models, particularly generative AI, require rigorous oversight to prevent unpredictable and potentially harmful outcomes. Ensuring data quality and consistency is critical, especially in sectors like finance. We help you navigate these challenges and capitalise on AI’s transformative potential.
AI Governance for Responsible Innovation
AI offers incredible opportunities but also significant risks. As AI becomes integral to business operations, strong governance frameworks are essential. At Calimere Point, we provide tailored solutions to ensure your AI initiatives are ethical, compliant, and aligned with your objectives.
More than compliance – a strategic advantage for organisations.
Our AI Governance Services
1. AI Governance Framework
Implement a tailored governance framework to effectively identify, measure, and manage AI risks.
2. Model Risk Evaluation
Assess and validate AI models to prevent issues like model drift and hallucination.
Combining our expertise in machine learning and generative AI to assess model performance through both qualitative and quantitative evaluations. We establish rigorous tracking and validation processes to identify and manage risks, ensuring accuracy and reliability. Our continuous and robust monitoring approach detects issues early, minimising risks such as model hallucinations and instability, and maintaining model integrity.
3. Data Integrity, Governance and Bias Management
Maintain data integrity and prevent biases for fair and responsible AI outcomes.
Ensuring high standards of input data quality for both training and operational datasets. We identify and address gaps or inaccuracies that could compromise AI performance. Our Bias Assessment and Mitigation Framework detects hidden biases and ensures datasets are representative and statistically sound, fostering fairness and transparency in AI outputs. By combining data governance with advanced statistical analysis, we help create AI systems that are accurate, fair, and compliant.
4. AI Competency Development and Knowledge Transfer
Building organisational AI literacy for informed governance.
Designed to simplify AI for cross-functional teams, providing them with the foundational knowledge to make informed decisions. As AI adoption and governance are often new to most organisations, we guide them through the complexities, ensuring that teams at all levels understand the core principles of AI. From technical staff to executive leadership, we ensure a unified understanding of AI, fostering cohesive decision-making and enabling organisations to embrace AI confidently and strategically.
Key challenges in AI and Generative AI Governance
AI governance is a complex and evolving field, presenting organisations with several critical challenges. As AI technologies advance, particularly with generative AI, managing their complexity becomes increasingly difficult. These models require specialised expertise to ensure they operate reliably and in compliance with both organisational goals and regulatory standards.
The benefits of implementing AI Governance
Mitigate Risks: Reduce risks related to accuracy, ethics, and compliance, building trust in AI outputs.
Regulatory Compliance: Align AI systems with local and global regulations, ensuring legal and ethical standards are met.
Risk Mitigation: Leverage frameworks like NIST RMF and OECD to assess AI systems, identify risks, and develop mitigation strategies.
ISO & EU AI Act Compliance: Ensure compliance with ISO/IEC 42001 and the EU AI Act, conducting conformity assessments and ensuring transparency.
