5 AI Data Security Governance Trends To Watch
By: Balaji Ganesan
As more and more businesses adopt the power of generative AI, data security governance (DSG) has never been more imperative. The impact of generative AI is deepening, prompting organizations to reevaluate their data security, privacy, and governance strategies relating to generative AI apps.
Given this point, here are the trends enterprises should keep in mind as the landscape of AI continues to evolve.
1. Safeguarding Sensitive Generative AI Data Becomes Paramount
As generative AI evolves rapidly, organizations will increasingly harness sensitive data for training AI models, Large Language Models (LLMs), and associated embeddings in vector databases. While this unlocks new possibilities, it raises concerns about sensitive data leakage. Data that would have traditionally been locked in silos can now be leveraged through generative AI use cases. While the business value is clear, generative AI can also open an enterprise to greater risk by exposing sensitive data if not properly governed and secured. Unlike securing data in standard databases, securing data inputs and outputs from generative AI applications will require organizations to adapt their data security and governance frameworks to accommodate the new architecture.
Many enterprises are moving beyond experimentation and increasingly using sensitive data to train the underlying AI and Large Language models. This transformation can only be successful with a unified data security governance strategy, and data leaders must apply controls to sensitive data and AI in a consistent and scalable manner.
See also: Data Governance Concerns in the Age of AI
2. Global Momentum in AI Regulation & Data Residency
Privacy, data security, and compliance are taking center stage, driven by new regulations such as the EU AI Act and existing mandates such as GDPR. The impact of AI and data knows no borders, making global compliance a top priority. Organizations must proactively stay ahead of regulatory changes to ensure that their data and AI practices remain secure and compliant worldwide. Implementing control systems to automate safeguards (versus purely relying on educating people) and enhance trust and security in a unified, automated, and global manner is critical.
3. Persistent Growth of Multi-Cloud Adoption and the Need for Comprehensive Data Governance and Security Strategy
Driven by innovation, multi-cloud adoption and the “best-of-breed” approach will continue gaining momentum. Organizations will continue leveraging cloud partners and services to address specific use cases. Microsoft has taken a head start with its partnership with Open AI. Very recently, Google has offered features never seen before with its recent changes to Gemini. Beyond the cloud providers, the open-source ecosystem is blooming with new LLMs and applications being released.
In diverse worlds, enterprises must think about data governance and security holistically to reduce the complexity and costs of effectively managing data and AI models. Scalable solutions will be essential to support modern applications, co-pilots, and collaboration across cloud boundaries while maintaining consistent data security and governance.
4. Convergence of a Unified Data Security & Governance Strategy
As organizations increase their reliance on diverse tools, the need for a unified approach to DSG has never been greater. Data security in modern data and AI requires an end-to-end lifecycle approach that starts with finding, classifying, and tagging where sensitive data might be located in your data estate. Then, you must comprehensively secure data access and continuously audit and monitor your data security posture. Instead of building data governance and security in every tool, a unified approach ensures that security and governance controls are consistently applied across the entire data estate, irrespective of an organization’s size or data. This approach offers the flexibility to address evolving compliance and security requirements by identifying sensitive data, deploying robust data policies, and ensuring access transparency at scale.
5. Shift from Centralized Command and Control to Federated Data Governance
The continued shift of data to the cloud and AI and increased demand for data products and new AI applications change the typical paradigm of IT as the primary technology manager and operator in organizations. A new model is emerging that federates the appropriate stewardship responsibilities between central data teams in IT and data and analytical stewards resident inside business units. DSG solutions must adopt and empower this co-ownership paradigm to deliver both the analytical velocity and the centralized governance and oversight needed for organizations to achieve their responsible and trusted data use mandates…
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