The Emergence of Artificial Intelligence in Security
Artificial intelligence (AI) being used for cybersecurity is a dream come true to most professionals in the security and risk management field. We’ve seen promises of robots and machines that can analyze data and mimic human learning since the early days of science fiction, but now this has started to cross over into reality. Some well-known areas of use are predicting natural disasters, at home assistants, and even automated driving. However, one of the most prevalent areas where we’ve seen this growth is with cybersecurity.
It’s not uncommon to see cybersecurity solution providers offering AI and machine learning (ML) to remove most of the manual functions required in cyber threat detection and mitigation. AI can even help in forecasting and preventing risk.
Why AI for Cybersecurity?
We all have our strengths, but data analysis is often better left to the machines. A lot of cybersecurity work requires scrolling through massive amounts of data to look for any anomalies or indicators or a threat. It’s like looking for a needle in a haystack, but the needle may not exist… and you have digital eye strain.
It’s a lot of data to go through, and this is all prior to actually encountering a threat. AI and ML systems ensure that your security team can focus most of their attention only on what is flagged as a potential risk. This is beneficial in reducing fatigue, which in turn helps reduce the chance of human error.
In an analogy used by IBM, a security analyst can be compared to a police officer. They undergo training and schooling that allows them to develop a certain level of intuition and understand what a threat might be and how to respond. While this is effective there are certain limits, such as where an officer might need a police dog to catch speedy criminals or use heightened sense to detect drugs. In this same way, analysts have limits that they need to use AI to detect or perform functions outside of human capability.
How AI Can be Used
While AI is still being developed and its use being explored within cybersecurity, what already exists is pretty substantial. Even the government has seen the potential. Read the Department of Defense’s strategy for utilizing AI for security here.
Automated Network Analysis
Defending your network and scanning for threats should be a top priority, and any assistance you can get should be welcomed. This is because most cyber attacks are over the network, so having a good front line defense will prevent the spread of malware and any data breaches. Anomaly detection, keyword matching, data monitoring, and other methods will alert you to any suspicious communications that have gotten past your firewall.
Email Protection
Due to the widespread use of email and the simplicity of attacks, email has become one of the greatest vulnerabilities to cybersecurity. One of the most prevalent email threats is phishing attacks. These are when attackers try and steal your personal information by pretending to be someone else. An example is you might get an email that looks like your Verizon bill or message from a coworker, but really it’s a fake and an attacker is trying to get you to click on a bad link or download an attachment.
Unfortunately, a lot of these attacks get past spam filter and firewall protections. AI solutions step in and are actually able to scan emails and compare behavior and patterns in data that help them recognize ransomware, spear phishing attacks, etc. What’s neat is these solutions can inspect links and look at things such as sentence structure, word choice, or anything that might raise suspicion. These emails would then be flagged for manual review, saving a lot of time and reducing risk.
Antivirus
Antivirus programs are historically signature-based. As malware is discovered, file names and indicators of compromise are cataloged in antivirus engines. As each file enters a network or computer, it’s scanned against the signature list and quarantined or deleted when there’s a match. This is a security issue because, in a report by SentinelOne, “only 50% had been previously submitted to malware repositories. And of that half submitted, only 20% had corresponding signatures from existing anti-virus engines.”
Antivirus systems operating with AI focus on detecting unusual behavior by programs rather than matching signatures. This is typically more efficient because most malware is designed to function differently than is the standard operation of the computer system, and they can be detected from these actions. This allows these AI-based antivirus systems to detect zero-day exploits and other previously unseen/cataloged malware.
A Word of Caution
Although the use of new technology can really supplement an impressive and holistic defense, things are still in the early stages. AI should be used as a support and not something to rely heavily upon. Unfortunately, with all of the hype around AI, companies may just be trying to advertise the use of it without fully understanding how to use it effectively.
After all, AI is only as good as the data it’s trained on. Many organization will have to choose and identify the data to train their solution, helping it to identify examples of clean data and what contains malware. If this is done incorrectly, you can easily be lulled into a false sense of security when your program isn’t raising any flags for you to review – but in actuality, it could just be missing them.
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