Concentric Inc. this week launched a service that makes it simpler to translate data and risk insights surfaced by the Semantic Intelligence data access governance platform into specific remediation, monitoring and privacy management tasks.
Karthik Krishnan, Concentric CEO, said Concentric Exchange is the first anonymous sharing service that both simplifies and improves data security and access governance.
Concentric’s Semantic Intelligence platform leverages deep learning algorithms and other advanced tools to automatically categorize data and determine its relative criticality to the business. It compares documents of the same type to identify risks from, for example, oversharing, third-party access, wrong location or misclassification. A risk distance analysis tool creates the baseline for each data category to identify any security anomalies in individual files.
Concentric Exchange adds a service through which organizations can anonymously share the insights and risks used to implement data security and access policies. It is hosted on Concentric MIND, a deep-learning-as-a-service platform designed to facilitate secure and anonymous sharing. As a result, organizations can benefit from the expertise and experience of other Concentric customers to interpret risks and content signals, implement remediation activities and classify sensitive data.
The service eliminates the need for cybersecurity teams to set up what are often kludgy processes for sharing insights and policies, noted Krishnan. The service itself only takes about 30 minutes to set up, he added.
Cybersecurity teams, in general, need to find easier ways to collaborate at a time when it’s clear cybercriminals are sharing not only intelligence but also the tools necessary to create exploits. Arguably, the only way chronically understaffed cybersecurity teams will be able to level the playing field is to rely more upon algorithms and other forms of artificial intelligence (AI) to identify threats.
It may take some time for AI platforms to learn the unique attributes of every environment, but as those algorithms become more effective their ability to augment cybersecurity professionals steadily increases. Unlike cybersecurity professionals, however, algorithms never forget what they learn, take a day off or quit to take a different job that pays better. Deep learning algorithms that rely on neural networks to understand an IT environment tend to be more effective than comparatively simpler machine learning algorithms.
Of course, it’s not likely algorithms will replace the need for cybersecurity professionals any time soon. However, as it becomes more apparent that many cybersecurity jobs will remain unfilled, the need for AI platforms becomes more apparent. Many cybersecurity professionals will eventually conclude they would rather work for an organization willing to invest in AI than continue to rely on manual processes that increase cybersecurity fatigue.
Naturally, there’s still a lot of skepticism when it comes to all things AI. It takes some time for most cybersecurity professionals to put their faith in any kind of AI model. Nevertheless, AI in form or another is coming to cybersecurity. The only thing left to determine is to what degree.
