E D I T O R ' S Q U E S T I O N
IWONA RAJCA, SENIOR SOLUTION ENGINEER EMEA AT PROTEGRITY
Balancing the security and usability of data is indeed a balancing act and it’ s a challenge to get it right. Too stringent controls often translate to locking down information sources and platforms. In companies that subscribe to this approach any access to data is governed by a multi-step approval process. If we believe that data is the new gold, this can stifle or even kill any innovation. In fact, security concerns are one of the top reasons why companies are reluctant to adopt cloud technologies and modernise.
Recently, we have seen that approach in action as everyone in the corporate world rushed to block access to Copilots and AI chats on employees’ computers. On the other end of the spectrum, lenient governance and underdeveloped controls are the two main ingredients of data breaches.
Operating in a complex regulatory landscape adds another dimension to this dilemma. To avoid fines, it may appear more attractive to keep everything locked down, as the saying goes, it’ s better to be safe than sorry. Prioritising a system’ s security comes first. data management should come down to what constitutes an acceptable risk for your organisation.
Top global banks have widely adopted solutions that automatically apply security guardrails to data. In a well-designed system all data coming in is protected, so that it resides on the server or on the database in an encrypted form, that no one in the organisation or an outside attacker can decipher.
The data is only re-identified for authorised users when needed: within their Business Intelligence reports or customer applications. That’ s the core of data-centric security, something that we at Protegrity have been building and perfecting over decades. AI has been a great motor for enhancing this capability: it’ s now possible to automatically detect if any given data is sensitive and protect it accordingly.
It’ s AI at its best, enhancing existing processes by improving their quality – AI models get better over time – and reducing cost and effort by automating manual tasks. Together, this approach builds a strong control framework over the most sensitive data resources and provides a programmatic answer to the security verses usability conversation.
Companies can learn from the banking industry and how they approach nonfinancial risks – such as data privacy or IT platform risk. In their world, risk is quantifiable. You can translate this idea to other sectors: the strategy around
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