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Trust and Transparency in your AI use cases with
AI Governance
Without proper governance and visibility, AI use cases will create a chaotic and unmanageable mess.
Every piece of data has a purpose and a journey, undergoing transformations, cleansing, and other processes. Along the way, it may pass through multiple hands, and it’s essential to know who worked with it and how it was handled. Recording this journey provides a complete history of the data's lifecycle. With data provenance, you can track data flows, monitor changes, and assess the sensitivity of the data feeding your AI models, ensuring transparency, accountability, and compliance.
Centralize AI use cases and models for a consistent view and streamlined documentation. Get a clear overview of all AI systems, including their purpose, the data they rely on, and the validation processes they follow. Track who has access to and can manipulate the data, while maintaining thorough documentation of datasets.
Simplify risk identification and evaluation for AI systems in compliance with regulatory frameworks like the AI Act. Assess each use case from multiple perspectives using predefined, expert-designed assessment logic. With Dawiso, risk analysts can access AI system documentation, examine the data used for training, and ensure that the system aligns with the intended use and regulatory requirements.
A semantic layer is a bridge between raw data and users, translating complex data structures into business-friendly terms. It standardizes definitions and relationships, ensuring consistent interpretation of data and models across the organization. By providing a unified framework, it allows teams to access and understand data in a way that aligns with business concepts, making data analysis and AI model usage more intuitive and reliable.
The AI Act is the European Union's first comprehensive legislation regulating artificial intelligence (AI). It takes a risk-based approach, aiming to create a regulatory framework that identifies and mitigates risks associated with AI systems. The Act categorizes AI systems into different risk levels: minimal, limited, high, and unacceptable, with specific regulatory requirements corresponding to each level.
The AI Act aims to establish a regulatory framework that ensures AI systems are used responsibly and safely. Its primary objectives are:
1. Risk-based assessment of AI use cases
The AI Act aims to identify high-risk AI systems rather than applying a blanket approach.
2. Centralized list of AI systems
The Act emphasizes the importance of maintaining a centralized catalog of AI use cases, ensuring all systems and their associated information are easily traceable. This includes thorough documentation of the datasets used to train AI models, promoting transparency and accountability.
The AI Act outlines the requirements that must be met, but does not dictate the methods for achieving those requirements.
AI governance is crucial for ensuring transparency, accountability, and trust in AI systems. It helps organizations direct, monitor, and manage AI activities, addressing key risks such as regulatory risk (non-compliance with laws), reputational risk (e.g., chatbots spreading harmful content), and operational risk (system failures or vulnerabilities).
Failure to comply with the prohibition of AI practices outlined in Article 5 of the AI Act will result in administrative fines. These fines may reach up to €35,000,000 or, if the offender is a company, up to 7% of its total worldwide annual turnover for the preceding financial year, whichever is greater. [Article 99: Penalties | EU Artificial Intelligence Act]
Dawiso is a metadata management platform designed for AI governance, providing a centralized hub for documenting and managing all your AI use cases. It offers key capabilities to support compliance and transparency:
1. Risk-based assessment of AI use cases:
Evaluate data and identify high-risk datasets and AI models using tools like metadata management, lineage tracking, and a dedicated AI model assessment questionnaire. Gain insights into data flows and ensure responsible AI practices.
2. Catalog and document AI use cases:
Maintain a centralized repository to document detailed information, such as training datasets, model versions, and metadata. Built-in traceability ensures systems and data are easily trackable, supporting both compliance and trust in AI systems.
Dawiso enables governance across the entire AI lifecycle—from defining business objectives and selecting data to documenting models, testing outcomes, and ensuring regulatory compliance during production and verification.
Simplify regulatory preparedness with features for system documentation, training dataset compliance assessments, and evidence exports—all in one platform.
What does AI governance look like at Dawiso? You can check out the interactive guide at the top of the page.
Take action now to be prepared for what is coming. The deadlines are fast approaching.
Businesses must prepare for key compliance deadlines:
Immediate Focus: Companies should prioritize understanding the classification of their AI systems, particularly those deemed "high-risk," and begin assessing their compliance requirements.
Upcoming Deadlines:
February 2, 2025 – Prohibited AI Practices: Bans on manipulative, deceptive, or exploitative AI systems take effect. Businesses must act now to ensure compliance with Article 5.
August 2, 2025 – General-Purpose AI Systems: Requirements for versatile AI models, such as text or image generators, come into force.
Main Compliance Deadline: The majority of the Act's requirements will come into full effect on August 2, 2026, providing a two-year transition period for businesses to align their operations with the regulations.
High-Risk Systems: Obligations for high-risk AI systems, as outlined in Annex III of the Act, require businesses to implement robust governance practices, conduct risk assessments, and ensure detailed documentation and traceability of AI systems.
Preparation Now: Given the complexity and scope of the Act, businesses are advised to act swiftly to evaluate their AI systems, set up governance structures, and ensure the necessary documentation processes are in place.
Proactively addressing these steps will help organizations stay ahead of the curve and avoid non-compliance challenges.
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