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How Will AI and Cybersecurity Integrate?

The approaching relationship of cybersecurity and machine learning

3x3 Rubik's Cube

In an age where data saturation has reached an all-time high, sifting over the results and translating it to useable results is reaching well beyond the human scope. Data analysis tools and collection methods allow for the interception of typhoons of data. Too much, however, becomes difficult to manage.

Then, take this to the cybersecurity realm and the problems can amplify. With the number of attacks and methods hackers use, agencies need agility and proactive strategies to guard people and their networks.

But to see how AI might integrate with cybersecurity dynamics (and data gathering) we have to look at what’s relevant.

Examples of AI in Cybersecurity and IT Environments

So you now know that AI is used in various IT and cybersecurity settings. But to give you a better idea of its implementation, we’ll provide some contemporary examples.

AI for Threat Location/Hunting

In cybersecurity, AI is useful for predictive learning. In other words, discovering a threat behavior before it attacks a network based on learned data. With AI, it can discover threat techniques on previously established tactics. As new threats and attack attempts occur, AI continues to learn and adapt (ideally).

Monitoring for Networks/Data Centers

Given how precious data is today and how routinely hackers go after it, giving it the best protection is critical. Today, AI typically operates as smart monitoring, tracking the flow and travel of connections in an effort to identify suspicious behaviors.

However, AI can do much more. Properly implemented, it can monitor resource and bandwidth usage, equipment checks, and network stability. AI can create alerts if problems occur in these sectors.

Threat Management

AI helps build profiles on threat actors and vulnerabilities within a network. This enables IT and security teams to better manage threats and tiers based on data gathered and compiled by AI.

The examples thus far are a few ways AI is observed (and used) in modern work settings. How might it work in the future, though?

Working with AI

There is a lot of potential with artificial intelligence in the IT environment. The question is how much can it automate, and how much should it automate? Catching redundancies en masse is important for efficiency reasons. The trained human eye, however, can always observe things AI could miss.

But, there’s an argument to be made against that too considering human error is a major part of cybersecurity breaches. Could then an AI with powerful behavior tools and machine learning identify, say, phishing schemes? Where an individual may lapse in judgment because of social engineering techniques, AI could capture specific strategies and flag them as threats. Additionally, AI would instantly access massive data pools that aggregate malicious emails.

In other words, it’s drawing from a bigger “warehouse” of information to “know” what a malicious email might be.

But that’s only one small idea for the potential AI could have when working in cybersecurity environments.

Drawbacks of AI

While drawing clear lines about the pros and cons of AI is yet to be seen, there are some potential downsides. One is that although AI is a tool, it’s a tool useable by anyone, malicious actors included. Additionally, reliance on a single “solution” never bodes well for cybersecurity.

For instance, reliance on an automated technology creates a “hands off” approach to IT and cybersecurity management. But it’s that scrutiny, knowledge, and expertise so critical to the functions of technology like AI.

Also, consider that AI is rooted in behavior techniques and pattern recognition. In an age of social schemes and phishing engineering, these are dangerous. The main is because AI can be used for automatically generated content.

A few examples? Drawing from a database of thousands of captured faces, AI can “randomly generate” a portfolio picture, often with convincing results. AI is also moving towards written content generation. You’ve probably seen examples already with “suggested words” and “smart editing.” Programs like Grammarly also make use of it in small capacity.

So, a phishing campaign could take advantage of artificial people with simulated words. How easy will it be to distinguish the two?

Just as well, AI’s utilization for automated attacks using smart data is a potential threat. For all the good it brings, we must remain aware of its downsides too.

For more information, you can contact Bytagig on ways to protect your business, even with remote services.

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