Artificial intelligence is one of the fastest-growing technology, impacting nearly all industries.
However, despite the remarkable progress in machine learning, AI is yet to get humans out of the loop regarding cybersecurity threats. Businesses still face significant threats from malware, APTs, and ransomware.
More than a million new virus samples are created every day, making it difficult to track where the threats are coming from. However, generative AI capabilities and cutting-edge technologies offer a real potential for boosting security. Here’s a look at ways AI and advanced technologies can be used for cybersecurity:
User and Entity Behavioral Analytics
Generative AI algorithms can spot suspicious activity or behavior patterns and flag them for further examination. This is known as user and entity behavior analytics or UEBA. The technology relies on AI’s remarkable pattern recognition capabilities. Organizations can employ AI to identify anomalies and potential data breaches.
AI can monitor what the suspicious files do at different times, allowing the organization to monitor activity at 5 am versus 9 pm versus midnight. Such comparisons, featuring time-tied behavioral attributes, lean on the cutting edge and help organizations identify potential threats and reduce false positives. Behavioral analytics can feature automatic notifications that inform the IT team to investigate potential threats and prevent a fully-fledged attack.
One area where UEBA can boost security is in the gaming industry, where sportsbooks and casino providers can monitor customer account activity. AI and cutting-edge technologies can detect abnormal betting and withdrawal requests, new devices, and locations, among other markets. Notifications can be sent to the registered customer for further verification. Leading betting sites already use AI to flag suspicious activity and fortify access control.
Flagging Advanced Persistent Threats
AI and cutting-edge technologies can scan the entire network to detect advanced persistent threats (ATPs). Cybersecurity criminals often set up their attacks by lurking on the network to gather credentials, information, privileges, and leverage before gaining access to private systems. ATPs can learn user behavior and identify vulnerabilities that hackers can use to attack. Fortunately, AI capabilities can monitor all activity and draw activity patterns.
Activity patterns can then be analyzed to detect the presence of bad actors. AI and cutting-edge technologies use targeted attack analytics that rely on machine learning to correlate across control points. The analytics may not pinpoint the issue but will inform you of suspicious activity in the machine environment. A breakdown of the behavior and a chance to provide feedback also allow the machine to learn and make future decisions.
Detecting ATPs gives the organization the head start needed to act proactively. The IT security team can scan the environment, investigate potential threats, and strengthen encryption protocols to keep sensitive data safe. AI can also buffer security by generating robust cryptographic keys and encryption algorithms that keep changing to resist brute-force attacks.
Evolving with Adaptive Security Solutions
Cyber threats evolve daily, calling for adaptive solutions to keep up with emerging challenges. Generative AI and cutting-edge capabilities can learn and adapt in real-time, unlike traditional solutions that could only deal with past threats. AI uses incoming data and processes and learns from all interactions. Implementing AI can identify emerging threats and take preemptive actions to mitigate the potential effects of the attack.
Adaptive security relies on machine learning, which allows different companies to enjoy 100% security using the same solution despite their different deployment environments. Cybersecurity strategies vary from organization to organization, so the security solutions must adapt to each environment. Adaptive security allows the solution to learn and understand the environment before deploying self-learning models specifically for that user.
A more practical example of adaptive security is using AI to flag and report files with specific names and characters, such as “confidential.” When the same algorithms are deployed in a government agency where many files are saved as confidential, they’ll adapt and stop flagging such files. The system can also notice an increase in the creation of certain files and track their movement within the network, notifying the user of the suspicious activity.
Enhancing Cybersecurity Training and Simulation
AI and cutting-edge capabilities can help address the human factor, which remains a significant vulnerability in data security. Organizations can use AI to simulate cyberattack scenarios and help employees recognize and respond to common threats. AI provides immersive training experiences that mimic real-world attacks. Such simulations can enhance attack responsiveness and bring awareness to how hackers and viruses work.
Frequent drills also help to reduce human error and expose negligent habits that can compromise organization security. Generative AI and other technologies like virtual reality can be used to immerse employees into situations that are indistinguishable from a real threat. Learning from near-real experiences helps to strengthen the company’s defense strategies. AI can also provide predictive environments for future attacks.
Predictive environments provide insights for dealing with evolving cyber threats before they emerge. Employees can stay ahead of threats by learning how to deal with different situations. Simulations provide vivid examples that bring awareness to the potential damage the company can endure in case of an attack. Reminding employees of the constant threats and how to navigate them can boost security.
The Future of AI In Cybersecurity
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