AI is redefining how organizations manage data, amplifying the need for robust Information Governance by Design (IGBD) principles. With AI systems, particularly large language models (LLMs), relying on vast volumes of high-quality data, ensuring proper governance isn’t just advisable—it’s critical for compliance, ethical accountability, and operational efficiency.
In laypersons’ terms, IGBD means building rules and processes for handling information directly into how systems, workflows, and policies are created. It ensures that data is always managed securely, complies with laws, and stays organized from the start.
Key elements of IGBD include defining who can access data, how long it's kept, and how it's protected, making it easier to avoid risks and meet business goals PETs provide a suite of technologies that help to address these challenges, particularly, as they relate to AI processes.
Not surprisingly, Privacy-Enhancing Technologies (PETs), that enable organizations to integrate privacy, compliance, and transparency into the core of AI systems are a key IGBD facilitator. These technologies are not merely tools for risk mitigation; they are enablers of responsible and scalable AI practices, ensuring that governance isn’t an afterthought but a built-in principle.
A recent survey by Deloitte highlights the urgency. According to this study, 62% of enterprises cite data privacy as the primary challenge in deploying AI systems, yet only 15% of organizations have integrated PETs into their AI workflows, revealing a stark gap in governance readiness.
This disconnect points to a critical opportunity to embed PETs into IG frameworks, ensuring AI systems align with both regulatory and ethical imperatives.
The Data Privacy Challenge in AI
AI relies heavily on both structured and unstructured data, but this dependency introduces significant challenges for organizations. Unstructured data, which includes videos, emails, and images, now makes up over 80% of enterprise data, according to a recent survey by the Association for Intelligent Information Management (AIIM). While this data holds valuable insights, it also presents substantial compliance risks, making its management a critical concern. Without robust governance practices, the sheer volume of unstructured data can overwhelm organizations, leading to inefficiencies and vulnerabilities.
In addition to the overload of unstructured data, many organizations face risks related to data misuse and lack of transparency. Studies indicate that 56% of companies struggle with unauthorized data use in AI training, raising both regulatory and ethical red flags. Furthermore, only 28% of organizations report having full visibility into their data lineage, which is essential for ensuring AI reliability and accountability. These gaps underscore the urgent need for stronger data governance frameworks to mitigate risks and build trustworthy AI systems.
What Are Some Critical PETs?
Federated Learning, a machine learning technique that allows multiple parties to collaboratively train an AI model on decentralized data without sharing the raw data itself – A McKinsey report found that federated learning can reduce data exposure risks by up to 90% in collaborative AI projects!
Multi-party Computation, a cryptographic technique that allows multiple parties to jointly compute a function over their inputs while keeping those inputs private from each other, and is often used by financial institutions to analyze fraud patterns across institutions without exposing sensitive customer data.
Differential Privacy, which adds controlled noise (random variations (noise) to data or computations in a way that obscures individual data points) while preserving the overall statistical properties or insights of the dataset, a technique that can be used to personalize customer experiences while safeguarding their personal information.
Homomorphic Encryption, which allows computations to be performed on encrypted data without decrypting it, ensuring data privacy throughout the process. The results of these computations, when decrypted, match the outcomes as if they were performed on the unencrypted data.
How PETs Transform Information Governance by Design
Below are some examples of how PETs play a critical role in facilitating IGBD:
Data Privacy and Security. PETs such as homomorphic encryption and secure multi-party computation enable secure data sharing and analysis by allowing organizations to process and analyze sensitive data without exposing or compromising it. This aligns with IGBD goals of safeguarding data, minimizing risks, and enabling trustworthy analytics and can help organizations enhance data security, support collaboration, and build stakeholder trust.
Data Quality and Reducing ROT. Redundant, obsolete, and trivial (ROT) data undermines AI accuracy. The effective use of PETs, combined with IG practices such as strategies to improve version control, the use of consistent naming conventions, and retention-schedule-based deletion, can defensibly delete ROT data while preserving critical datasets.
AI Workflow Transparency. Data lineage, or the ability to create a detailed tracking of data's origins, transformations, and movements throughout its lifecycle, is a prerequisite of effective IGBD. This capability is a cornerstone of effective IGBD because it provides transparency, accountability, and control over data processes. Understanding data lineage is also essential for ensuring data accuracy, maintaining regulatory compliance, and fostering trust among stakeholders.
Compliance Controls. Regulations like the EU’s AI Act impose strict requirements for governance over AI data usage, emphasizing transparency, accountability, and privacy. PETs like homomorphic encryption and federated learning help organizations streamline compliance by automating processes such as data classification, retention, and access control, ensuring that AI systems adhere to legal boundaries without compromising efficiency. The importance of this integration is highlighted in a Deloitte study, which found that companies incorporating PETs into their compliance workflows experienced a 25% reduction in regulatory penalties, underscoring the value of PETs in reducing risks and enhancing operational resilience.
Ethical AI Development. Bias in AI is a persistent issue, with 48% of models found to exhibit unintended biases, per an MIT study. PETs like federated learning, which helps detect bias patterns across diverse data sources while maintaining the privacy of individual datasets, support ethical AI development by enabling bias detection and mitigation during model training while maintaining data privacy.
By embedding PETs into IGBD frameworks, organizations can transform unstructured data from a liability into a strategic asset. This synergy ensures AI systems that are secure, ethical, and trustworthy, enabling enterprises to unlock AI’s full potential while navigating an increasingly complex data landscape.
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