Table Contents
Table Of Contents
Table Of Contents
Empower your organization with a roadmap to responsible AI governance.
Enterprises are swiftly embracing the artificial intelligence (AI) surge. In a recent McKinsey Global Survey, 65% of respondents reported that their organizations regularly use generative AI (GenAI). A robust AI strategy is now essential for business.
As companies move up this adoption curve, they face a perennial challenge: data that is incomplete, inaccurate, insecure and therefore untrustworthy. This brings AI data management to the forefront, as you can’t have an effective and compliant AI strategy or build an AI governance framework without reliable data. Organizations will need to demonstrate how they are using AI safely and responsibly and be able to easily answer why their AI models have made certain decisions.
AI Data Management Needs
The pressure on enterprises globally to use AI for business advantage is accelerating rapidly and that means understanding how this impacts the business in real time to help ensure it adheres to changing regulations. The underlying technology foundation supporting a strong data management strategy requires AI-powered automation to scale. An integrated, cloud-native, modular platform can deliver support to de-risk AI initiatives and enhance compliance with new AI regulations like the European Union (EU) AI Act.
To meet the demands of modern data and AI governance, a data and AI model management solution should scale as the business grows and intelligently automate governance and data management as a flexible, extensible, agile platform. An innovative and robust solution capable of dealing with the complexities of managing and navigating AI responsibly is crucial.
The Landscape of AI Legislation
As the adoption of GenAI systems explodes across enterprises globally, regulations follow. Many countries are examining how best to manage AI regulation and the first region to publish new legislation is Europe.
The EU AI Act
The EU AI Act is a comprehensive legal framework for AI regulation planned by the EU. Its aim is to ensure the safety and fundamental rights of citizens and businesses. This Act classifies AI systems based on their associated risks and implements specific rules for each category, fostering the use of AI that is lawful, ethical and robust across the region.
The EU AI Act can compel non-EU countries and companies to evaluate and potentially adjust their AI strategies and services if they wish to operate in the EU market. This approach could standardize controls across regions, enabling some organizations to gain a competitive edge by aligning better with regulations and managing international partnerships more harmoniously.
Singapore Model AI Governance Framework
Singapore has developed a proposed framework for GenAI, which expands on the existing Model Governance Framework that covers traditional AI. The proposed framework aims to create a trusted environment for AI development globally, emphasizing principles like explainability, transparency and fairness.
Many countries are in various stages of evolution in regulating AI and have differing views of how best to do it. Consequently, there is no clear consensus on how best to regulate AI either from a global standpoint or consistently, which allows innovation while curtailing potentially harmful uses of AI.
AI Governance Challenges
Many companies today have inefficient data management processes that result from disorganized pipelines, inadequate data observability and a lack of governance controls. These perennial problems can make GenAI outputs unfit for use, which could lead to poor decisions, frustrated customers and significant policy compliance violations.
The growing number of countries and regions enforcing new AI regulations demonstrates that data leaders must understand how to comply with an array of new laws across relevant territories where they do business. This could be a bewildering checklist of risk management challenges for international organizations operating across borders. As such, the evolving nature of GenAI means that businesses must stay abreast of relevant laws and regulations, including AI compliance laws as well as intellectual property rights and data protection, to ensure compliance and mitigate legal risks. Robust data governance that ensures the availability and security of high-quality, reliable data throughout the enterprise is critical.
This complex scenario illustrates some of the crucial pillars of modern data governance, including managing data, AI risk, compliance, data quality, data observability, data sharing, data democratization, data cataloging and data lineage for integration and transparency.
Let’s take a closer look at these capabilities and how they support modern data governance.
Data Integration
The disparate nature of data sources in use in modern technology networks means that data could be spread across silos due to legacy, fragmented data governance technologies that require significant manual intervention and hinder the company’s ability to fuel AI initiatives with trusted data. Embracing AI requires shifting from legacy on-premises data governance systems towards an agile, scalable and cost-effective cloud-native solution to keep up with growing data demand.
Data Quality and Observability
There are two core challenges to ensuring high data quality: extending data observability programs for GenAI applications to better spot quality issues, such as by setting minimum thresholds for unstructured content to be included in GenAI applications, and developing interventions across the data life cycle to fix issues.
Data Bias
32% of CDOs in our Informatica “CDO Insights 2024: Charting a Course to AI Readiness” survey saw bias as a top challenge in adopting or planning to adopt GenAI technology. Bias can come in different forms, such as statistical bias and societal bias.
Data Privacy
The lack of oversight in data privacy and protection can pose a more significant challenge and lead to a loss of trust in AI adoption, for example, when using personally identifiable information (PII) inappropriately. It's important to enable security controls, clarify data ownership and establish rights for proper data use.
Responsible AI Governance Framework Principles
Ethical frameworks are crucial to guide AI development and deployment. Explore 11 responsible AI principles that serve as the foundation for building sustainable AI practices that are compliant and meet societal expectations.
Fairness means that AI systems should provide equitable outcomes for all users, regardless of their background or demographics.
Bias Mitigation requires detecting and reducing or eliminating biases in AI systems that can lead to unfair treatment of individuals or groups.
Transparency involves the processes by which AI systems are designed and deployed, which includes clear disclosure about AI's decision processes and capabilities.
Explainability is the ability of AI systems to detail, in an understandable way, the processes and factors they use to make a decision.
Privacy requires ensuring that personal information is kept secure and is only used in ways to which people have agreed.
Data Protection entails the legal and secure handling of data.
Accountability means that the developers, operators and deployers of AI systems are responsible for the consequences of their operation, regardless of AI's autonomous capabilities.
Governance establishes the frameworks, policies and standards to manage AI development and deployment responsibly, focusing on ensuring compliance with ethical and legal standards.
Safety involves designing and implementing systems that reliably handle data without errors that could lead to harmful decisions.
Security entails protecting AI systems from malicious attacks and ensures that they are reliable and resilient in the face of attempts to tamper with or manipulate their operation.
Societal Impact includes understanding and managing the broad effects of AI technology on society, such as ethical standards, societal benefit and avoiding potential negative consequences on social structures.
Data Leader’s Checklist
Examine your organization’s readiness to adopt AI technologies and stay ahead of the curve with this handy checklist.
Perform an external gap analysis to understand what data and AI governance solutions exist and identify what methods of manual governance are performed today that are unreliable and don’t scale.
Identify missing key capabilities, such as robust security and privacy controls for handling sensitive data.
Establish reliable standards for data quality and ensure sufficient transparency.
Verify dependable policy enforcement that aligns with your data strategy for proper data and AI usage.
Review and understand scope, implications and requirements for navigating the latest AI regulations, such as the EU AI Act, with key stakeholders across the enterprise.
Develop a detailed risk management framework that addresses the specific exposure associated with unreliable AI and data management practices or unknowns (e.g., user access rights).
Establish and communicate clear data and AI policies regarding data usage, AI model deployment and regulatory compliance.
Audit existing data management practices by regularly reviewing and refining data management processes to eliminate inefficiencies and disorganized data pipelines.
Upskill employees and provide data literacy programs by rolling out continuous education focused on AI and data literacy for relevant staff.
Develop training programs on bias mitigation and data ethics for employees.
Oversee the implementation of fairness, transparency and accountability measures.
Enforce privacy and data protection principles across the organization.
Regularly audit and update governance frameworks to ensure compliance and societal impact considerations.
Promote a culture of data-driven decision-making, continuous learning and adaptation to AI-driven technologies.
Conduct a high-level assessment of current data governance capabilities and identify gaps that can be addressed by an advanced solution.
Develop a detailed strategy that incorporates AI capabilities to manage, discover and govern data.
- Prepare a change management plan to address potential human and process modifications.
Success Stories
Informatica has worked with customers worldwide to help them make better business decisions through responsible AI-powered data governance. These two examples from the healthcare and telecom industries demonstrate our longstanding commitment to strategic partnerships that deliver through technological advances.
Biopharma’s Swift AI Compliance Upgrade
Challenge
A multinational pharmaceutical and healthcare company wanted to realize their vision of using AI to develop and market new drugs more quickly, optimize their product portfolio and become the first biopharma company to implement responsible AI on a large scale. The organization faced a complex data and AI environment with over 150 disparate data models, leading to significant compliance challenges.
Solution
By leveraging the capabilities of Informatica Intelligent Data Management Cloud™ (IDMC), Biopharma now has a unified and accurate view of data across the organization, enabling a connected data landscape that supports the delivery of trusted, high-quality and compliant data to AI applications. Integrated AI-powered data management capabilities have allowed the company to transition to a modern data mesh architecture. This architecture features near real-time data availability for analysis and advanced analytics, achieved through standardized, governed and automated data flows.
Outcomes
The organization has significantly accelerated its generation of insights through the easier availability of trustworthy, safe and secure data. Robust compliance measures have enhanced the integrity and reliability of this data, improving the accuracy of AI-generated insights. Additionally, AI-driven automation has bolstered the organization’s capacity to govern disparate data effectively and adapt quickly to changing AI regulations, all while reducing technology costs.
AI-Driven Data Unification in Telecom
Challenge
A leading European multinational telecommunication company faced a growing need to integrate diverse data sources from various regions to responsibly cater to different stakeholders and use cases. Navigating a vast and intricate data environment, characterized by over 1,000 enterprise applications, the organization had a critical need to govern diverse data spanning enterprise networks and partners.
Solution
To address its data challenges, the organization partnered with Informatica to implement a federated data governance model. This model adheres to the FAIR principles, ensuring that data products are findable, accessible, interoperable and reusable. Informatica's solutions have empowered the company to democratize high-quality, governed and secure data products, making them globally accessible to partners and customers. Additionally, Informatica has facilitated timely access to high-quality, cross-functional data, significantly accelerating the transformation of the end-to-end customer journey
Outcomes
The implementation of Informatica's solutions has led to readily available and trusted data, drastically reducing the time to realize business value. This enhancement in data accessibility and reliability has also strengthened the organization’s adherence to contractual and regulatory compliance requirements, solidifying its standing in a competitive and fast-evolving telecommunications industry.
Master Responsible AI Governance with Informatica
Take advantage of a comprehensive data management platform that transforms vast and complex enterprise data into actionable business value and enables effective AI compliance.
Informatica Intelligent Data Management Cloud™ (IDMC) plays a central role in de-risking AI initiatives and enhancing compliance with regulations such as the EU AI Act. As shown in Figure 1, the comprehensive solution offers data governance with privacy controls, data quality improvement and AI-powered data cataloging to ensure the transparency, reliability and integrity of data. This is all done through a data management platform that is multi-vendor, multi-cloud (including AWS, Azure and Google) and hybrid, supporting on-premises and cloud-based data storage and management. Through automated data management tasks and seamless data integration, IDMC increases operational efficiency and creates a single source of truth.
CLAIRE®, Informatica’s unified metadata intelligence, introduces a natural language (NL)-based experience to IDMC. This dramatically simplifies tasks such as data discovery, integration, quality, governance and master data management. The Informatica CLAIRE GPT, a GenAI-powered version of our pioneering AI engine CLAIRE, helps simplify, accelerate and optimize data management operations, driving enormous gains in productivity for data teams.
Here are just a few ways IDMC can support and advance your responsible AI governance journey:
Empowers data users with an easy-to-use interface
Improves data quality and transparency by automating repetitive tasks such as debugging, testing, refactoring and documentation
Streamlines data pipeline creation with fully automated workflows
Automates the linkage of governance policies directly to your enterprise data, ensuring that compliance (managing relevant AI regulations) and best practices are embedded throughout your data management processes (as illustrated in Figure 2)
Start driving superior business outcomes through responsible AI governance, with Informatica as your trusted partner.
Looking for more tips and tricks to strengthen your organization’s approach to responsible AI governance? Download this eBook to fast-track your progress.
