NetworkTigers discusses AI in cybersecurity.
Artificial intelligence (AI) is one of the biggest buzzwords in modern technology. The concept of artificial intelligence dates back to the 1960s, although most of its applications only gained momentum recently. This is especially true of cybersecurity. The key reason why AI is so special boils down to the fact that AI task automation is fast and efficient. How does this apply to cybersecurity?
One may also think of the internet, the miles of cabling deployed to access the websites of one’s choice in a blink of an eye. This is just one aspect of cybersecurity. An important part that is forgotten is the physical security involved. It includes protecting the server rooms and places where confidential data are stored. All parties are at risk from a cyberattack, be it an individual or a business. It is important to mitigate these risks and not allow them to spread further as and when it is discovered. Now that the scope of cybersecurity is covered, how can it be defined? Cybersecurity or information security is the practice of safeguarding the integrity, confidentiality, and availability of information. It comprises a set of tools, risk mitigation procedures, training, technologies, and protocols designed to protect networks, devices, data and programs from threat actors or unauthorized access.
Uses of AI in cybersecurity
AI task automation plays a significant role in cybersecurity. For example, threat hunting and penetration testing are laborious and time-consuming tasks. Many of the smaller steps and processes are repetitive. Artificial Intelligence tools free up your team’s time to spend on more important tasks.
Another area in Cybersecurity where AI tools are used is for filtering false positives. For example, security teams are flooded with what are often false warnings and alerts from the security tools that they use. Alerts come from firewalls, routers, and network intrusion devices. Network engineers set up manual filters through each one in order to triage them according to relevance. The time-consuming procedures involved in checking false positives can mean the real threats and warnings go unnoticed. By using the right AI tools, these false positives are filtered out, leaving only the real and legitimate ones to be examined.
Businesses and corporations now realize that multiple security tools mean multiple opportunities for threat actors. Rather than using ten firewalls, it is a better strategy to use the minimum number and employ AI to learn and predict when and where security threats are likely to arise.
The Sub-Fields of Artificial Intelligence and its Applications
Machine learning algorithms use statistics to find patterns in large amounts of data. Data may encompass numbers, clicks, images, and any and all digital elements. This data is processed by the machine learning algorithm using statistical and mathematical techniques on vast data sets to find patterns. These patterns are then fed into an AI tool, which uses them to predict future threats. Machine learning also powers recommendation systems and search engines that are popular today.
These are a set of algorithms, inspired and modeled after the human brain, used to examine data and find patterns connecting them. It is similar to machine learning in that neural networks are also used to work through large datasets to find patterns. The fundamental difference is that neural networks replicate the thinking process of the human brain, by monitoring and examining neuronic activity.
Computer vision is a blanket term for any computations that involve visual imagery, be it videos, icons, images or anything with pixels. In cybersecurity, this is mostly used to secure the physical assets of an organization. For example, CCTV cameras monitor spaces visually. Computer vision may be deployed with facial recognition technology to further enhance security.
AI in cybersecurity: the conclusion
Artificial intelligence is not yet widely used in cybersecurity but holds great promise for several applications such as:
- Triaging and filtering out false positives in security alerts, alleviating “alert fatigue” affecting network teams.
- Automate the processes behind threat hunting and penetration testing exercises.
- AI can be used to model the threat landscape more quickly and efficiently than manual modeling.
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