EDITOR’ S QUESTION
violating compliance regulations or exposing sensitive information to undue risk.
Organisations need to manage their data infrastructure with a dual focus on availability and security. A unified and intelligent data management system is essential.
Such a system would seamlessly integrate data from diverse sources, on-premises systems, cloud environments, and edge devices while maintaining strict control over data privacy and compliance requirements.
By classifying and categorising data, organisations can ensure that Generative AI models only access relevant and authorised information, eliminating the risk of accidental exposure of sensitive data.
Data sovereignty further complicates compliance, especially for organisations operating across multiple geographies.
FADI KANAFANI, GENERAL MANAGER, SOFTSERVE
As the adoption of Generative AI spreads across the enterprises and shadow AI begins to emerge from various departments, how should the usage of data be managed to ensure it is available for Generative AI use cases and yet does not violate data privacy and data compliance policies?
point, ensuring data integrity and reducing risks of unauthorised access.
To address the challenges and opportunities presented by Generative AI adoption across enterprises, particularly in managing data usage while upholding data privacy and compliance, businesses must consider the following principles.
With enterprises embracing Generative AI, shadow AI, which are AI solutions developed independently by other departments, can complicate data governance. It is, therefore, essential to establish robust frameworks that ensure data accessibility for innovation without compromising compliance.
SoftServe emphasises adopting synthetic data solutions as a privacy-preserving alternative. This approach enables organisations to share and utilise data for Generative AI use cases without exposing sensitive information, ensuring both compliance and innovation coexist seamlessly.
Additionally, embedding data governance strategies at the outset of Generative AI initiatives ensures compliance with region-specific regulations such as GDPR or regional data protection laws.
AI and machine learning tools can automate compliance checks and monitor data flows to detect anomalies. Behavioural analytics, for instance, can identify deviations from normal data usage patterns, flagging potential breaches or misuse.
Departments often adopt shadow AI due to inefficiencies in centralised systems. Providing teams with AI-powered tools that integrate seamlessly with existing workflows reduces the need for unsanctioned solutions.
For example, SoftServe’ s co-development of tailored AI tools with clients ensures alignment with corporate governance while delivering operational value.
A critical step is implementing a Zero Trust Architecture to manage data access and sharing securely. This model enforces authentication at every interaction
A critical step is implementing Zero Trust architecture to manage data access and sharing securely.
Integrating AI-driven cloud-native tools can further enhance scalability and data management. Multi-cloud environments allow enterprises to store sensitive data in compliance-safe zones while leveraging the scalability of other platforms for non-sensitive operations.
SoftServe’ s expertise in cloud-native and multicloud solutions ensures seamless data operations across platforms.
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