The internet plays a vital role in our lives. And with every second that ticks off, there’s a new thing happening. No doubt, it is the most helpful tool ever-developed, there are risks associated with security and privacy.

With automation taking place at a rapid level, everything has become more convenient than ever. Cybersecurity to be specific, has improved drastically due to the advent of AI and Machine Learning. These tools can help recognize different patterns for use in security systems and improvise cybersecurity standards.

Artificial Intelligence and Machine Learning

AI and ML are strategies that help machines make decisions based on data and patterns as input without the need of pre-programming. Using AI, processes can be automated without the need for human intervention. Data, patterns, and algorithms act as input for AI, which then help machines perform a specific task. This functionality in turn helps accuracy and optimizes tasks and processes. Using AI and ML, data can be mined, patterns can be drawn and decisions can be made based on the same.

AI and ML, together, has helped automate cybersecurity and reduce risks associated with cyber attacks. Organizations can better predict the outcome of a certain task/activity and take adequate measures to avoid the instances of data theft, malware attack, and more. Organizations can further leverage the capabilities of AI to improve data classification, clustering, processing, filtering, and management related tasks.

Ways AI and ML can help ensure cybersecurity

Threat Intelligence

With AI and ML intruding the cybersecurity space, detecting threats associated with cyberattacks has become much easier than earlier. Traditional security measures like using signatures and indicators to identify threats, were effective against previously-encountered issues, but as threats continue to evolve, the need for advanced security measures is on an ever-high. And, AI and ML are exactly what is needed. Proactive threat-intelligence becomes a reality as AI and ML intrude the cybersecurity space. This helps organizations in taking adequate preventive measures to rule out the chances of cyberattacks.

Risk Management

Businesses and individuals are exposed to risks on an everyday basis which they struggle to manage at once. Traditional risk management strategies involve waiting for the attacks to happen and then deploying strategies to contain the same. AI and ML on the other hand, make risk management easier using techniques such as User and Event Behavioural Analysis. This helps identify and analyze abnormal user accounts behaviour , endpoint network behaviour, and server behaviour to protect the systems against vulnerabilities, even before they are detected.

Hardware Maintenance

Traditional hardware maintenance involves the role of manpower which may not be very efficient and secure. Data centre optimization and monitoring of processes like backup power, power consumption, bandwidth usage, and security management can be a tough task to handle manually. AI and ML, here, can help ease down the responsibilities and improve the effectiveness and security of the hardware. Furthermore, it also helps reduce costs associated with maintenance by alerting the service personnel about when to change the equipment.

The final word

AI and ML have vast applications and some of them are relevant to cybersecurity as well. With proactive threat intelligence, risk management, and hardware maintenance capabilities as discussed above, the perfect combination of AI and ML is going to benefit both organizations and individuals alike.