20 Tools And Strategies For Safe And Efficient AI System Data Sharing

Companies increasingly rely on artificial intelligence tools to drive decision-making and streamline operations. But to achieve the best ROI in terms of cost and efficiency benefits, secure and smooth data sharing among AI systems is critical.

The AI data rush is driving greater urgency for event-driven integration. This entails the combination of the data transformation and connectivity attributes of an integration platform as a service with the real-time, dynamic choreography of an event broker and event mesh. Only with this enterprise architecture pattern will AI systems be able to truly work together to offer real-time digital experiences.

Stay skeptical—deepfakes and AI impersonations of security and leadership personnel pose risks to data sharing across systems. This can create horrifying avenues to access infrastructure, funds and data. An efficient strategy to combat this is to implement a true zero-trust architecture, always verifying and validating that the human you are conversing with is who they say they are. -

Data governance policies for all data are the most critical aspect to remember. When AI is working on behalf of a user, it should be able to access only the data that the user has been granted access to. Granular permission-based controls are typically a feature of any trustworthy data management platform; a similar philosophy should be adopted when leveraging AI systems on internal data. -

The strategy requires planning around data, process and application controls. A simplified way to think about it is to consider where data is stored, how it’s being updated and which datasets require anonymization or should be kept siloed. From here, additional controls are set in terms of governance and data authorization. At the infrastructure level, best practices are needed. -

AI systems are fundamentally data applications. They need a centralized foundation with data governance and security for managing both inputs and outputs for differing AI use cases. Governance models should focus on sourcing, data definitions, classifications and use restrictions. Secure data sharing can be achieved through anonymization, entitlements, access policies, controls and approvals. -

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