Exclusive: Exploring predictive analytics and cybersecurity

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The world of cybersecurity is constantly changing. IT professionals today face the challenging task of adapting to new threats and hacking techniques as they arise. Fast reactions are essential to a network’s security, yet responding quickly is rarely easy. Predictive analytics could help.

Predictive analytics is a subset of artificial intelligence (AI) that analyses data to predict future events and outcomes. It’s seen remarkable adoption, with the potential to nearly triple in five years, but mostly in the realm of improving efficiency. Recently, though, predictive analytics has started gaining traction among cybersecurity professionals.

Addressing issues before they arise

Predictive analytics could assist with one of cybersecurity’s greatest challenges — adapting to changing threats. By analysing cybercrime trends, a predictive analytics engine could predict what kinds of attacks businesses should anticipate. They could then prepare appropriately, ensuring new hacking techniques don’t catch them off guard.

Predicting the next wave of cybercrime techniques may be a lofty goal, but predictive analytics can serve a more feasible purpose, too. These programs can detect data breaches before they occur. The effectiveness of any cybersecurity response depends on its timeliness and there’s no timelier action than a preemptive one.

According to a recent IBM report, it takes 280 days on average to identify and contain a breach. If cybersecurity teams knew what was coming, that number could plummet. Security professionals could stop hacks before they happen. Even if these systems don’t predict every attack, preventing just a few represents a considerable damage reduction.

Concerns with predictive analytics

For all its potential, predictive analytics still isn’t perfect. All AI algorithms are subject to flaws, and these can slip past users’ eyes until it’s too late. For example, people recently discovered that a medical predictive analytics algorithm exhibited considerable racial bias in prioritising patients.

Social issues may not be a leading concern in cybersecurity algorithms, but the same reliability problems persist. If there’s any misleading information in the data that trains an algorithm, the system could teach itself to make erroneous predictions. The nature of machine learning expands and exaggerates any flaws with the information at hand.

Perhaps the most significant issue facing predictive analytics is a matter of data. A system needs a considerable pool of high-quality information to make accurate predictions. That’s a resource not every company has. In fact, a recent NIST report found that just 51% of companies have established a baseline of expected data flows.

Successful implementation of predictive analytics

These concerns don’t render predictive analytics unusable but show where companies can and should improve it. If cybersecurity teams hope to use these tools, they should first address their data. They should have access to large pools of high-quality, ideally well-organised information if predictive analytics is to work.

As these tools become more prominent, the teams that develop them have to be careful about preventing mistakes like bias. If developers can ensure they avoid misleading these algorithms, they can help avoid faulty predictions.

Finally, companies should remember that as helpful as predictive analytics can be, it can’t replace human workers. Cybersecurity is a multifaceted, complex practice with a lot of room for error. These tools should supplement the work of human IT professionals, not replace it.

Predictive analytics could be a turning point for cybersecurity

If predictive analytics continues on its current course, it could become one of the most disruptive technologies in cybersecurity. As these algorithms improve and more companies adopt them, they’ll become the norm. In the future, it may be rare to see a professional cybersecurity solution that doesn’t use predictive analytics.

Like any technology, predictive analytics is not a solution to every problem. It won’t eliminate cybercrime, nor will it end the need for seasoned human cybersecurity workers. It could, however, turn the tide in the fight against cybercrime.

Devin Partida is a technology writer and the Editor-in-Chief of the digital magazine, ReHack.com. To read more from Devin, check out the site.

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