AI and ML Combating Cyber Threats

Are the uses of artificial intelligence (AI) and machine learning (ML) in the cybersecurity industry a high-tech toolbox sufficient to counter advanced technology? Can this inseparable duo eliminate inefficient human behavior and perception from the equation as we fight cyber-crime? Can security systems be educated to recognize behavioral anomalies as they happen?

In recent years, we have seen an increase in advanced cyber-attacks. Cyber criminals utilize sophisticated technology to breach the digital border and exploit enterprise security weaknesses. No sector feels safe; security professionals do everything they can to close security gaps and bolster their cyber defense. As new technologies emerge at a record pace, cyber security professionals are 'chasing their tail' as they learn how they work and adopt best practices to protect against cyber risks.

An advanced toolkit is required to combat advanced technology. Artificial intelligence (AI) and machine learning (ML) have become relevant in the cybersecurity industry. Can these inseparable companions play a significant role in eradicating inefficient human behavior and perception from the equation as cyber criminals are combated? Can security systems be educated to detect behavioral changes as soon as they happen?

Cyber security, AI, and machine learning are becoming increasingly important.

The “lethal” damage to business reputation and revenue caused by cyber attacks is getting worse as more firms go digital. Between the third quarter of 2021 and all of 2020, there will be a 17% increase in data breaches in the United States, according to Cybersecurity Ventures. Ransomware attacks occur every 11 seconds, resulting in a business downtime of over 20 days and a huge amount of ransoms paid, according to Cybersecurity Ventures.

While humans still play a significant role in cybersecurity today, technology is gradually catching up to us in several areas. AI and ML use existing behavior patterns based on past data and conclusions with minimum or no human intervention. These technologies assist defenders to respond to cyber incidents by increasing their accuracy and speed by analyzing massive amounts of data and building behavioral models to make accurate cyber attack predictions as new data emerges. According to Capgemini Research Institute, 69% of organizations cannot identify nor respond to cyber threats without AI, and the AI cybersecurity market is predicted to be worth $46.3 billion by 2027.

Time and cost-saving.

The speed at which a cybersecurity team responds to threats is a key measure of its effectiveness. Many attackers use sophisticated automation to accelerate their attack times significantly. In many cases, the security response lags behind the attack; teams are responding to successful attacks rather than preventing them.

These technologies can compile and distribute useful reports on attack information, as well as predict and prevent future attacks by processing massive amounts of data in real-time. They can also send alarms and generate defensive patches on their own, as soon as they spot a problem. According to Capgemini, the report found that using AI and ML technologies reduces IT costs by more than 10%. They are considered cost-effective technologies, as they reduce the threat detection and response effort.

Human nature is what makes people unsure.

Security teams should ensure that new infrastructure is functional when new technologies are stacked on top of old ones. Combining old and new systems is a challenging task. A proper configuration of the system, in which different layers interact with each other, is a difficult job for security professionals. Non-human, automated assistance can assist with these processes. There may be errors and omissions if security teams are forced to assess configuration security manually. Security teams may not notice issues, adjust settings, or apply updates if non-human assistance is not utilized.

According to research, there are a few ways in which AI and ML can help humans in overcoming threat alert fatigue. As the attack surface expands and diversifies, attacks increase. Many security systems issue a lot of proactive alerts to the security teams, requiring a human decision and action in an alert environment that is highly condensed. Due to a lack of people, training, and time, decision fatigue occurs. Using AI and ML technology, threats may be categorized, prioritized, and treated automatically.

Security teams face slow response times when dealing with unknown attacks, because they may remain well hidden, silent, and undiscovered for a long time. Using AI and ML, security professionals can discover commonalities between old and new threats, making it possible for them to forecast new risks and shorten lag times as a result of increased threat awareness.

The analyst can focus on what's critical.

There are many benefits to using AI and ML technologies in cybersecurity, as they decrease the time to detect and respond to cyber attacks; they detect cyber threats and suspicious behaviors effectively and improve businesses' cybersecurity posture.

There is a lot of excitement around AI and ML as upcoming digital security breakthroughs, but there is also a lot of exaggeration. Despite technology's development, the human is still the leader. In the technology age, we cannot eliminate human psychology from security. Despite human behavior, errors, and fatigue's massive effect on security, AI, ML, and people can work together to drastically reduce human inefficiency in the security formula.