How AI is Advancing Cybersecurity

There’s a never ending cycle between the measures cybersecurity providers introduce to prevent or remediate cyber threats and the tactics cyber criminals use to get around these security measures. As soon as a security company develops a way to mitigate the latest threat, attackers develop a new threat to take its place.

Artificial intelligence has emerged as a critical tool cybersecurity companies leverage to stay ahead of the curve. It makes defensive measures stronger and response times faster, but it’s not a perfect solution. AI is not a replacement for human intelligence—especially when it comes to identifying and mitigating threats—but it does advance cybersecurity in powerful ways.

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Machine learning identifies unknown threats

Machine learning is a component of artificial intelligence that helps cybersecurity tools operate more efficiently. It analyzes data and recognizes patterns so that it can detect changes in behavior. In this way, machine learning is able to identify and address threats before a human security engineer even realizes something is amiss.

Technology for today and the future

Michael Knight, Co-Founder and Head of Marketing at Incorporation Insight, says machine learning is one of the most useful AI components for improving cybersecurity. “Machine learning,” he explains, “provides an organization with the foresight to detect cyberattacks in advance, giving them ample time to strategize ways to counteract future and known threats.”

This technology enables cybersecurity tools to pinpoint attacks with more accuracy than a human security engineer. Machine learning keeps cybersecurity systems running at peak performance, and it will likely be the ticket to maintaining a strong cybersecurity infrastructure in the future.

“Historically,” Knight continues, “technology relied on past findings when developing strategies and preventative measures, causing organizations to adapt slowly to changes. As time passes, cyber threats become more sophisticated and evolved, and using traditional techniques to combat these threats will no longer suffice. The use of AI enables computers to adapt quickly and prevent threats.”

Machine learning security tools

Not only does machine learning make cybersecurity operations more effective in the present, but it also ensures you won’t fall victim to an attack as cybercriminals’ tactics become more advanced. You’ll be hard-pressed to find a security tool that doesn’t include machine learning capabilities in some way because it’s an invaluable element in any cybersecurity strategy.

IBM QRadar SIEM, for example, detects and prioritizes threats company-wide. It aggregates information from sources across your network, including endpoints, servers, and applications. It then studies this information to determine how specific events are related and initiates a response if necessary. This kind of threat intelligence and analysis wouldn’t be possible without QRadar’s machine learning capabilities.

AI improves incident response

In addition to improving threat detection, artificial intelligence makes it possible for cybersecurity teams to respond to incidents faster and with more precision.

Evaluate threats more quickly

As digital transformation takes over the business world, security teams are tasked with processing and protecting unprecedented amounts of data. Artificial intelligence makes it possible to sift through this data and identify potential threats immediately. Before AI, cybersecurity tools used signature tracing techniques to match system activity to that of known threats. Not only did this limit the cybersecurity defensive measures a company could take, but it also meant the processes were slow and inefficient.

Harriet Chan, Co-Founder and Marketing Director of CocoFinder, says that behavioral analysis helps develop profiles of an organization’s applications by processing high volumes of data. This helps with prioritization and response to security alerts and gets to the root of the problem to avoid future issues.

“It is essential to understand the impact of various security tools and processes you have employed to maintain a strong security posture,” Chan says. “AI can help understand where your infosec program has strengths and where it has gaps.”

With behavioral analysis, cybersecurity tools are able to identify any signs of a threat—known or unknown—without wasting any time. There are often legitimate warning signs to imminent threats, and AI is able to detect these amid false positives. Security professionals are then able to use this knowledge to prioritize the threats and respond to them accordingly without worrying the information is unreliable.

Related: Best User and Entity Behavior Analytics (UEBA) Tools

Automate defense measures

Security orchestration, automation, and response (SOAR) tools have gained popularity among cybersecurity strategies since they were first introduced in 2017. In fact, the SOAR market is expected to reach $2.3 billion by 2025, according to a 2019 report from Report Buyer.

These tools are impactful because they use artificial intelligence to reduce the amount of human intervention needed to act on security threats across an ever-increasing surface area. By extension, SOAR platforms also minimize the risk of human error. This means cybersecurity automation helps with productivity as well as risk reduction.

SecOps teams can leverage automation to make their jobs easier. SOAR products allow them to set rules and create workflows, which act as a foundation for all processes. Then, they can focus on fixing the root cause of any detected threats rather than only addressing the symptoms.

SOAR tools also help with vulnerability management by testing integrations and configurations to identify areas of risk. Many tools can fix simple configurations automatically, which means security engineers can turn their attention to bigger priorities.

AI won’t replace human cybersecurity pros

A common misconception about artificial intelligence is that it will one day replace human intelligence. AI is good for improving efficiency and accuracy when it comes to cybersecurity operations, but human security engineers are still necessary for strategy and collaborative problem solving.

The human perspective, flawed as it may be, is also needed to discern good data from bad data. Artificial intelligence is based on data samples, so the AI is bad if the data is biased, inaccurate, or flawed. Only a human can tell whether the data is reliable. Security engineers must frequently review the data at the foundation of cybersecurity AI to ensure its reliability.

Bayt.com Co-Founder and CTO Akram Assaf explains that “most risks with AI come from organizations abandoning their responsibilities. You can’t just install a system and expect it to do the job for you. That’s not how it works, and even advanced cybersecurity systems powered by AI need to be regularly maintained and updated.”

Although artificial intelligence has tremendous benefits for your organization’s cybersecurity strategy, you still need people working to support it. Otherwise, you’ll be putting your weight on an unstable foundation.

Cybersecurity AI gone wrong

Aside from the staffing needs required to ensure successful AI implementation, it’s also important to understand the risks of using artificial intelligence for cybersecurity.

If an AI system is poorly implemented, it can be weaponized against a company in an attack. This could happen at the data level, where malicious actors manipulate the data sets that AI algorithms use to learn their behaviors. Vulnerabilities could also come from biases or gaps in the data. Hackers sometimes use a technique called neural fuzzing to determine where weaknesses lie in software that processes input data.

Thilo Huellmann, CTO at Levity.ai, explains this further:

“For learning purposes, machine learning systems rely on data. That is why it is critical for businesses to ensure the data’s dependability, integrity, and security; otherwise, erroneous forecasts may result. Hackers are aware of this and attempt to steal data from machine learning systems. They tamper with, corrupt, and poison that data to the point where the entire machine learning system collapses.”

“Businesses,” Huellmann continues, “should pay careful attention to the situation and take steps to reduce the danger. AI professionals should limit the amount of training data that cyber thieves can control and to what extent they can control it. Worse, you’ll have to defend all of your data sources because attackers can modify any data source you’re utilizing to train your machine learning algorithms. If you don’t do so, the chances of your machine learning training going crazy skyrocket.”

Hackers can also use artificial intelligence to power their cyberattacks. For example, they can build an intelligent piece of malware that can mutate to evade your cybersecurity defenses. These programs can think independently. If the attack isn’t successful in its first attempt, it adapts itself until it is able to compromise its target. This makes AI-fueled attacks especially worrisome.

To prevent your AI from working against you, it’s important to create safeguards. You should regularly evaluate the configurations of your devices and applications and monitor other areas of your cybersecurity infrastructure that aren’t directly-related to artificial intelligence tools. This is not only beneficial for your AI, but also for your security posture overall.

Protecting AI also includes establishing a chain of command and documented processes that can ensure swift action and accountability in the event of an attack.

Related: How AI is Mishandled to Become a Cybersecurity Risk

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Kaiti Norton
Kaiti Norton is a Nashville-based Content Writer for eSecurity Planet, Webopedia, and Small Business Computing. She is passionate about helping brands build genuine connections with their customers through relatable, research-based content.

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