Artificial intelligence is playing an increasingly significant role in cybersecurity. Learn how its applications are saving organizations from potential breaches.
Is there anything artificial intelligence (AI) can’t do?
Of course, every technology has its limitations. Yet, as people around the world continue to work remotely, the risk for cyber-attacks remains at an all-time high.
After all, cyber criminals are constantly looking for opportunities and pose a threat to all kinds of businesses and the consumers who patronize them. While these incidents may be hard to predict, more enterprises are turning to AI technology to boost their cybersecurity postures.
How might AI save the day? Read on for the answer!
While the digital era has opened up a world of opportunities, it has also exposed us to several threats – including putting our private information at risk. Truly, the need for cybersecurity has never been more important than it is now. Cyber criminals have the ability to reach their targets in any part of the world at any time and the last decade has seen a rise in identity theft, loss of money, and data breaches.
In response, AI tools and machine learning techniques are being developed and supported by artificial intelligence and machine learning to play a significant role in the fight against cybercrime.
Enterprises are taking notice. Organizations are starting to heavily invest in building AI systems that analyze large amounts of data sets that include malicious codes, malware codes, and anomalies, which helps cybersecurity teams identify possible threats.
When combined with machine learning, AI is playing an increasingly more significant role in cybersecurity. According to ZDNet, emerging security tools are analyzing big data from millions of cyber incidents and using that data for detecting threats like phishing schemes or new variants of malware.
Unfortunately, some cyber criminals are one step ahead of these detection efforts, tweaking their malware code so that security software doesn’t recognize it as malicious. Spotting every variation of malware poses a huge challenge; fortunately, this is where AI and ML step in. ML in particular is great for anti-malware cybersecurity solutions because it can draw upon information about any form of malware that’s been detected before. Therefore, when a new form of malware appears, the system can check it against the database, examine the code, and block the attack. This approach even works when malicious code is hidden within large amounts of harmless or useless code.
ML can also be deployed in other ways to pump up cybersecurity efforts. An AI-based network-monitoring tool can also track what users do on a daily basis. By analyzing this information, the AI can detect anomalies and react accordingly – a huge advantage in a constantly changing world.
No matter the application, AI and ML are also becoming major players due to the fact that they can stop threats in real time without impacting the day-to-day operations of the business. Further, these technologies can keep track of data that escapes the human eye, including the growing volume of video, chats, emails, and more.
AI is more than a buzzword. Entrepreneur outlines the following ways it is used to boost cybersecurity:
Sometimes, passwords are the only barrier between our accounts and cybercriminals. While innovations like biometric authentication are a step in the right direction, they are not super convenient and can still be hacked. Today, developers are using AI to make biometric authentication (body measurements and calculations related to human characteristics) even more accurate. Apple is using AI on its iPhone X devices for a technology called ‘Face ID.’ With this tech, infra-red sensors and neural engines are used to create sophisticated models of the user’s face by identifying key patterns and correlations. The AI software architecture can also work in different lighting and recognize the user despite changes like getting a new hairstyle, growing facial hair, or wearing a hat.
Phishing is one of the most commonly-used cyber-attack methods, with one in every 99 emails a phishing attack. Here, AI and ML can be used to detect and track more than 10,000 active phishing sources and react and resolve the situation much more quickly than humans can. Further, AI and ML can understand all types of phishing campaigns despite their geographic origins, making the technology even more vital to modern-day cybersecurity postures.
Many security postures are built to be reactive rather than proactive. Systems based on AI and ML fall under the latter by proactively searching for potential vulnerabilities in organizations’ information systems. They can do this by examining multiple factors, including patterns used by hackers, which are then analyzed to determine when and how a threat might make its way to vulnerable targets.
The creation of security policy and determining an organization’s network topography make up two important parts of network security. While these activities have traditionally been very time-consuming, AI is expediting these processes. It does this by observing and learning network traffic patterns, as well as suggesting security policies. Now, security personnel can focus on other areas of technological development and advancement.
ML algorithms have the ability to learn and create a pattern of a user’s behavior by analyzing how they typically use their device and online platforms. If the AI algorithm ever notices unusual activities that fall outside the user’s typical behavior, it can flag it as suspicious or even block the user. These activities can include large online purchases shipped to addresses other than the user’s, a sudden spike in document download from their archived folders, or a sudden change in their typing speed.
When it comes to employing AI for cybersecurity, there are a wide variety of tools being developed by cybersecurity companies. For uncovering hidden and targeted attacks, AI tools can apply AI and ML to the processes, knowledge, and capabilities of security experts and researchers. At the same time, these tools can analyze incidents in the network against incidents in an organization’s threat data lake, revealing suspicious activities at each endpoint.
Deep learning neural networks can be deployed to create algorithms that target the ‘DNA’ of malware and other cyber threats. Active self-defense products can detect and replicate digital antibody functions that identify and neutralize threats and viruses as they develop.
Finally, AI can be used to automatically investigate indicators of all compromises or exploits, alerting security analysts to threat incidents that need to be assessed. Cognitive reasoning can connect threat entities associated with genuine incidents such as malicious files, suspicious IP addresses, and malicious entities to create relationships between these entities.
No matter the way it’s used, it is clear that AI can provide critical insight about an incident and empower an organization to respond quickly.
The greater use of public cloud environments and a surge in devices and remote working means new cybersecurity risks. Stefanini works with businesses to respond to this new landscape and embrace best practices.
Are you looking to deploy the right technologies to strengthen your cybersecurity posture? We have you covered. Contact us today to speak with an expert!
Are you looking to deploy the right technologies to strengthen your cybersecurity posture? We have you covered. Contact us today!
See what's trending
Hi. Need help?