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When Ai grows, difficult questions about data privacy touch the mainstream.
Here in the UK, the data for data (use and access) of the government has just set its second reading in the Lower House. At the same time, the government navigates a legal challenge of AppleHe refuses that it has access to the private data of a customer if necessary. Concerning privacy, and the rules that guarantee it are investigated at the top.
The details are complex, but for most people it comes down to one simple question: “Are my data the costs of progress?”
From an enterprise perspective, we know that the technology used to accommodate, send and send and protect data is not necessarily built to prioritize privacy. Fail securitySiled data and the pain of outdated infrastructure can all be exposed an organization – in other words, those worries about the consumer can be well based.
This solving starts with how we collect and move data. If we can embed the privacy directly in the data collection process, can assess and manage data in real time, we can minimize the risk for both organization and consumers.
Director of Financial Services at Confluent.
Tackle patchwork -infrastructure
One of the reasons that are afraid facts Privacy has risen lately that it is extremely difficult to keep track of new regulations – especially considering how quickly AI evolves as a technology.
New and important legislation is constantly being introduced, with the data account of the UK only one of the many: the AI law of the EU, the deep synthesis provisions of China, and so on. But such laws try to regulate technology as it develops, and cannot necessarily precede the next great application of AI. The rule of law cannot always keep track of the sizzling pace of change, often aimed at compliance at the expense of proactive security measures.
As a result, many organizations will continue to depend on a unique mix of hardware and infrastructure. The company can function, but it cannot be robustly enforcing a consistent standard cyber security and data privacy.
Take, for example, the traditional problem of data fragmentation. The distribution of private data over multiple systems – each with their own possibilities, purposes and cyber security measures – makes it incredibly difficult to standardize the use of data. It can be duplicated, accessible in some systems and not others, or simply absent from where you would expect it.
The cracks in this armor are exacerbated by human intervention. Employees may have different access levels over different systems, retain permissions that they no longer use or not to understand how they can protect the data they have access once they have used them. All these things can cause a serious infringement.
Although regulations such as GDPR and HIPAA have been designed to mitigate these risks and to insist on the immediate implementation of data removal requests, the infrastructure with this data may not meet these requirements. Legacy technology slows both audits and response times and cannot paint an extensive image of which data should be deleted.
Introduction of data streaming
All these challenges make it clear that privacy cannot be a side issue. If you do not prioritize this on the points where data comes your ecosystem, it is incredibly difficult to work back to a point of robust compliance.
This is where real -time data streaming excels. When processing data when it arrives, data streaming prevents creating huge data sets that require slow, cumbersome batch processing and-masse. Being able to contextualize data and the protection it needs, even if that data is moving, that security and organizational work literally loads point by point.
Data streaming platforms (DSP) go one step further. If a platform that is specially built to coordinate the streaming of data about a company, they offer a single access point that integrates advanced security options through design.
End-to-end codingFor example, offers an extra protective layer while the data is in transmission. Likewise, tokenization can replace business -critical information with identification data that make transcription impossible. Another option is differential privacy – the introduction of mathematical noise in data sets, protecting individual identities without stopping a company that benefits from analyzing that data.
All these elements offer protection against countless potential concerns about cyber security in an organization – of accidental access to poor actors.
Security and the DSP
Research suggests that a large majority of British technical leaders – 91% – believes that data streaming cyber security and digital risk management improve.
Much of that value comes from the DSP that acts as the central nervous system of the organization, managing systems to keep everything in synchronization and to guarantee access to real -time data where it is needed. Privacy and security are baked in the system inherent, from the point of entry to the user point.
Because AI continues to settle as a standard, this is only becoming more important. The company The world speeds up – and the technology that we use to protect it must also accelerate.
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This article is produced as part of the TechRadarpro expert insight channel, where today we have the best and smartest spirits in the technology industry. The views expressed here are those of the author and are not necessarily those of TechRadarpro or Future PLC. If you are interested in contributing to find out more here: https://www.techradar.com/news/submit-your-story-techradar-pro
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