Managing information in the GenAI era

In 2024, data management looks a little different than it has in the past. This is largely because generative AI has brought with it new possibilities. However, as with most new opportunities, it has come with new challenges. Two data security experts to share their thoughts on the most important things every organisation needs to know when it comes to getting your data in order in the age of generative AI. 

James Greenwood is Regional Vice President of Technical Account Management at Tanium

“Data has become an organisation’s most valuable asset in the eyes of hackers. When we talk about records and information management, identifying all the endpoints an organisation’s data is stored on needs to be at the top of the list,” said Greenwood.

“In 2024, data is everywhere—from the server room to an employee’s smartwatch. This has made it increasingly difficult to protect data because so many endpoints are unknown. Tanium research found that up to 20% of endpoints are unknown in 94% of organisations, with many only auditing a small number of devices connected to their network at any given time. 

“Implementing effective information management strategies without visibility across all endpoints is impossible. If you can’t see it, you can’t secure it. But with traditional manual processes, that’s much easier said than done. In fact, it’s near impossible. 

“That’s why, when it comes to an information management strategy, organisations need autonomous real-time visibility to ensure continuous monitoring against threats. This enables an organisation to identify, and in some cases, remove sensitive information living on endpoints either unnecessarily or without protection.  

“Autonomous Endpoint Management can also improve process efficiency and ensure that all endpoints are patched in a timely manner to protect sensitive data. 

“When starting out on your information management cleanup, it's important to keep in mind four essential steps. Firstly, build a comprehensive inventory of your data storage locations by identifying all endpoints across your network and the sensitive data stored on them. Secondly, prioritise encrypting your data to safeguard it from unauthorised access.

“Thirdly, automate the real-time monitoring of each endpoint, ensuring rapid detection and response to any potential threats. And finally, streamline your data storage practices to minimise unnecessary accumulation. These measures not only enhance efficiency and cost-effectiveness but also help to mitigate risks, particularly in the event of a cyberattack.”

James Greenwood, Regional Vice President of Technical Account Management, Tanium

Alyssa Blackburn, Director, Information Management at AvePoint, points to a recent report conducted in partnership with the Association for Intelligent Information Management (AIIM) and Center for Information Policy Leadership (CIPL), which found that 77% of organisations acknowledge the need to implement new information management measures to keep pace with the increasing integration of AI into their operations.

“This has become particularly evident since the introduction of Microsoft’s Co-Pilot AI tools. We know though that AI is only as effective as the data and information it is provided. And old or sensitive content that has not been managed properly is going to become a major headache for organisations leveraging AI-driven technologies. 

“To be truly effective, an information management strategy must establish control over data and information throughout its entire lifecycle, from creation to eventual disposal or archival. Firstly, organisations need to understand the state of their data and information in order to make informed decisions. Once it has been thoroughly analysed, critical business data and information can be identified, managed, and protected accordingly.

“This process will also uncover Redundant, Obsolete, or Trivial (ROT) content that can be disposed of or stored in cheaper storage tiers to lower costs and reduce regulatory risk. It also helps to improve efficiencies by making sure information that is required on a regular basis can be found more easily. Further to this, it helps to ensure that the quality of the data and information we’re running AI tools across is as high as possible. 

“Secondly, organisations need to implement ongoing lifecycle management to reduce risks, particularly in relation to data breaches, regulatory non-compliance, and legal liabilities. Lifecycle management also supports better decision-making, improved system performance, and an overall more efficient information management environment.

“It involves setting rules and policies for different types of data based on their value, sensitivity, and compliance requirements and should be regularly reviewed and updated to adapt to changing business needs, evolving regulatory landscapes, and technological advancements. Again, an essential step when preparing to implement AI tools. 

“Lastly comes automation. With the explosion of data sets, manually tracking and classifying content has become an impossible task. This is when automating information lifecycle management and policies becomes your best friend. 

“For organisations looking to get their data in order, AI is going to present new data challenges whilst also creating significant opportunities. Organisations that embrace AI to help clean up their information management habits will reduce risk, gain valuable insights into their data, make more informed decisions, and maintain a competitive edge,“ concludes Blackburn.

Alyssa Blackburn, Director, Information Management, AvePoint.