March 12, 2021

How using multiple AI security technologies detects different threats

Artificial intelligence (AI) is truly a paradox. It was created by humans, but surpasses humans in different computing and predictive capabilities. It’s been called our greatest invention and best hope for the future, and the biggest threat to mankind. AI and cybersecurity is equally mystifying. AI threats are some of the most sophisticated attacks security professionals face. And a key part of the solution is to employ other AI security technologies to combat threats from hackers and their AI counterparts. AI security technologies are among the best ways that security teams can detect threats today. 

What is AI security technology? 

AI technology in cybersecurity has been used in some capacity for decades. Algorithms use machine learning to detect patterns and changes in patterns to alert threats. Likewise, password technology like multi-factor authentication employs AI to prevent compromised passwords and has been a staple of password security since they have become ubiquitous. Today, AI shares many of the same roles as security experts – like noticing potential threats, deploying initial protections, and combating threats that get through the defenses. 

AI security technology can be thought of as diverse types of codes and algorithms capable of learning on their own, memory, and applying rules, categories, and models to take on a wide variety of security tasks. AI is particularly good at sorting through huge amounts of data to detect patterns and abnormalities. AI systems can be thought of both as tools for increasing automation and augmenting human ability

How does AI technology help with cybersecurity?  

At Cetrom, our philosophy on cybersecurity comes down to employing top of the industry security experts and combining their talents and hard work with the most advanced security technology on the market. Artificial intelligence security and machine learning help check both those boxes by assisting security experts and acting as standalone protection. Advanced AI technology plays three key roles in cybersecurity including threat detection, threat response, and human augmentation. 

Intrusion detection systems. According to CPO Magazine, “cyber security companies are teaching AI systems to detect viruses and malware by using complex algorithms so AI can then run pattern recognition in software. AI systems can be trained to identify even the smallest behaviors of ransomware and malware attacks before it enters the system and then isolate them from that system.” Threat detection of malware and ransomware attacks is increasingly valuable as these types of attacks stay common. Two of the most frequently used threat detection systems are anomaly-based and signature-based (more information below) that use different strategies for observing and noticing attacks. 

Threat response. In addition to detecting potential threats, AI security can attack and tag threats once detected. From there, they can isolate the threat and take measures to prevent further damage or spread of the damage. AI is also valuable in this regard because they can work non-stop without drifting attention, coffee breaks, or sleep — though their virtues as a co-worker is debatable.

Human augmentation. Cybersecurity experts are constantly checking alarms, implementing new technology, helping end users, and testing the system. Human security experts have valuable, diverse skills that even the best AI can’t entirely match. But AI can act as a great first line of defense through multi-factor authentication, basic repetitive security checks, and sifting through large amounts of data. This allows humans to take on bigger picture roles and more complicated projects. 

Broad benefits of AI security systems

AI security can feel abstract and disconnected from day-to-day operations outside of the security team. But, simply put, AI has tangible benefits that reach beyond security. AI improves the efficiency of the cybersecurity team by augmenting their work and reducing the amount of tedious tasks they need to do. It also can save your firm a lot of money with a quick response in the event of a security breach. For example, the Cost of a Data Breach Report found that cybersecurity AI can decrease average costs by $230,000. AI can simply and easily protect passwords and data, which is a real benefit to employees and clients alike. 

Signature-based detection and anomaly-based detection 

Two common ways that AI is used to detect threats is through anomaly-based detection and signature-based detection. 

Anomaly-based detection: Anomaly-based detection systems can find and recognize pattern and data changes (called anomalies) and alert on them because they are new and out-of-place. These systems are particularly appealing because they can recognize threats that don’t have a known signature. They have the potential to find threats that are uncommon and novel and are thus even more appealing in a world where new, creative, threats emerge almost yearly. 

Signature-based detection: Signature-based detection relies on the knowledge that different types of attacks, whether malware, DoS, viruses, or phishing scams have different signatures that can be categorized and recognized. There is still machine-learning in this type of detection as AI can continue to compile signatures used. It was one of the first types of detection systems and has been successful in the past. However, unlike anomaly-based detection, it’s vulnerable to attacks that can hide their signature extremely well or are novel types of attacks. 

What are the limitations of AI security? 

The appeal and excitement of AI security and machine learning is understandably high. The notion of super advanced, learn-as-they-go robots have always enamored humans. AI security technologies that can detect and combat threats have a key role in the future of cybersecurity, but outsourcing most security tasks to AI is unrealistic at this point. There are a few limitations of using AI security systems. 

Resource intensive: AI technology can be expensive to initially buy or build, and then security staff will need to continue to update and maintain those systems. They also rely on large data sets that can be hard to find or create to train AI. On top of that, AI needs lots of memory and computing power. 

Reliant on humans: AI is only as good as the humans that create and maintain it. What’s more, humans often need to take over after an AI identifies and begins to counterattack. As counter-technology keeps adapting and improving, AI security needs to continually be updated and re-evaluated by humans.  

Susceptible to attack: AI is commonly used by hackers to attack AI security systems. There are some attacks that target AI in particular. Just as AI security learns from attacks in order to better defend, AI hackers can learn from security systems to more successfully attack. 90% of security experts think that attackers will use AI to target the company that they work for. 

What’s the bottom line? 

The benefits of using multiple AI security systems are huge. Between detecting and recognizing threats to immediately combatting those threats to augmenting the human impact in cybersecurity – AI plays an integral role in any high-powered security system today. Through different means, AI security can reduce the workload on employees, save your firm money, and protect sensitive information with little hassle. 

At Cetrom, we use different AI and machine learning technology to protect all of our clients. We see this like the guard dogs, motion detector cameras, and perimeter fence for on-site security. But whenever they’re needed, our security experts are available to respond to threats detected and react to complex issues that are beyond the scope of AI alone. Keep watching as AI security systems continue to expand and improve to keep up with the new, incoming threats from motivated and sophisticated hackers and their AI counterparts.

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