AI Governance & Risk Management Guide
Authored by EncompaaS - Dec 18, 2024
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As Generative AI (GenAI) revolutionises industries, the need for robust AI governance and risk management becomes critical. EncompaaS’ AI Governance & Risk Management Guide provides a data-centric framework to help enterprises navigate the complexities of AI deployment while safeguarding their data and ensuring compliance.
Why Download the Guide?
Comprehensive Risk Management
Understand the core risks associated with GenAI, including data breaches, compliance violations, and bias. Learn how AI governance addresses these challenges to protect your organisation from financial, legal, and reputational harm.
Data-Centric AI Governance
Explore EncompaaS’ data-centric approach to AI governance. Learn how our unique DUGU (Discover, Understand, Govern, Use) methodology ensures that your AI models are built on high-quality, well-governed data to produce accurate and ethical outcomes.
Enhancing AI with Trusted Data
Discover how EncompaaS’ platform enhances data quality and traceability, ensuring AI accuracy and reliability. Learn about our automated policies for data classification, privacy protection, and governance.
Ethical and Responsible AI
Gain insights into how incorporating Responsible AI principles from the outset can build trust and transparency with customers, employees, and stakeholders, fostering a culture of ethical AI usage.
Why Choose EncompaaS?
EncompaaS empowers organisations to achieve secure, responsible AI adoption through its intelligent information management platform. Our data-centric approach ensures compliance, innovation, and sustainable success with GenAI.
Ready to transform your AI governance strategy? Download the AI Governance & Risk Management Guide to explore how EncompaaS can help you navigate the future of AI with well-governed data.
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Featured Resources
- Video
Understand Your Data & Harness Generative AI
Jesse Todd (CEO, EncompaaS) and Jaimie Tilbrook (CPO, EncompaaS) delve into the critical challenges companies face in managing their vast data landscapes, emphasizing the importance of understanding what data is, where it is located, its context, and its value.
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