Artificial Intelligence combined with machine learning and the introduction of this technology into all industrial sectors has significantly revolutionised cybersecurity. Building automated security systems, employing natural language processing, identifying faces, and automatically detecting threats have all been improved by AI. But the question is, will AI eventually take over cybersecurity? A new cadre of experts will be essential to train for the AI technology, run it, and analyse the outcomes for IT teams to properly integrate AI technologies.

Artificial Intelligence has always driven the cybersecurity industry. Today, companies are looking for ways to use AI in the real world to provide a human-friendly threat management system so that users can interact with the system intuitively. The era of digital transformation has ushered in a new generation of cyber security. With increased activity and use, it’s critical to help clients understand the risk landscape, provide better visibility, and make informed decisions about what to prioritise.

AI Robotics
Today’s AI-powered robots, or at least those machines deemed as such, possess no natural general intelligence, but they are capable of solving problems and “thinking” in a limited capacity. From working on assembly lines at Tesla to teaching Japanese students English, examples of AI in the field of robotics are plentiful. Hanson Robotics is building humanoid robots with Artificial Intelligence for both the commercial and consumer markets.

The Hanson-created Sophia is an incredibly advanced social-learning robot. Through AI, Sophia can efficiently communicate with natural language and use facial expressions to convey human-like emotions. The robot has even accepted citizenship from Saudi Arabia.

Softbank Robotics developed a humanoid robot known as Pepper, which is equipped with an “emotion engine” that makes it “capable of recognizing faces and basic human emotions.” Standing at 4 feet tall, Pepper can operate in more than a dozen languages and has a touch screen attached to support communication. Miso Robotics builds robotic kitchen assistants. The company has released Flippy 2, the second generation of its AI-equipped robot that helps with kitchen automation for tasks like frying food.

Cybersecurity in Industry 4.0
Cybersecurity in Industry 4.0 cannot be addressed as it has been in traditional computing environments. There are far too many devices and associated challenges. Consider monitoring security alerts for millions of connected devices around the world. Because IIoT devices have limited computing power, they cannot run security solutions.

This is where Artificial Intelligence and machine learning come into play. ML can compensate for the lack of security teams. While processing large amounts of data, AI can aid in discovering devices and hidden patterns. In the IoT ecosystem, ML can help in tracking incoming and outgoing traffic for any deviations in behaviour. Security administrators can send alarms to alert them to suspicious traffic if a threat or anomaly is discovered.

AI and machine learning can be used to create lightweight endpoint detection technologies. This can be a life-saving solution, especially when IoT devices lack processing power and require less resource- intensive behaviour-based detection capabilities.

AI and machine learning (ML) technologies are a two-edged sword. To avoid detection, threat actors can use AI to automate things like target selection and attack timing. Human impersonation and AI-powered password guessing are all risks. Misuse of AI and ML is a concerning trend that appears to be growing in tandem with its widespread adoption in the business world. Enterprises must pay close attention to any potential malicious exploitation of their AI systems.

Revamping cybersecurity with AI & ML will offer advanced trends in cybersecurity education, trends that are pushing the industry forward. An in-depth look at how AI and machine learning have changed the world of cybersecurity and how these technologies have revolutionised cybersecurity programs. The increasing threat dynamic associated with cyber-attacks and attacks on personal data has compelled the need to revamp cybersecurity with AI & ML.

The Impact of Artificial Intelligence and Machine Learning on cybersecurity
AI is the ability of machines to perform tasks that would normally require human intelligence, such as recognising patterns or making decisions. ML is a subfield of AI that deals with the ability of machines to learn from data and improve their performance over time.

AI and ML can be used to create more sophisticated and effective cybersecurity solutions. For example, by using ML, cybersecurity solutions can be constantly updated and improved as new threats emerge. This means that enterprises can stay one step ahead of the hackers.

In addition, AI and ML can be used to automate various cybersecurity tasks, such as identifying and blocking malicious activity. This can free up security teams to focus on more strategic tasks, such as improving their overall cybersecurity posture.

Contribution of AI & ML
AI and ML are playing an increasingly important role in cybersecurity. AI can be used to help identify and protect against potential threats, while ML can be used to help learn from past incidents and improve future security. Both AI and ML are helping to make the cybersecurity landscape more effective and efficient.

Organisations are using AI and ML for a variety of tasks, including identifying malicious activity, protecting against phishing attacks, and improving incident response. AI is also being used to create better antivirus software and to develop new ways to fight malware.

AI and ML are two of the most important tools in the cybersecurity toolkit. As the threats we face continue to evolve, AI and ML will play an even more important role in helping us stay one step ahead.

There are many reasons businesses should consider using AI and ML for cybersecurity purposes. These are just a few of the most important ones. With the ever-increasing threat landscape, it’s clear that AI and ML are becoming more and more essential tools in the fight against cybercrime.

1. Machine learning can help identify patterns that human analysts might miss.
2. AI can automate tedious tasks, like sorting through large volumes of data, so that human analysts can focus on more important tasks.
3. By constantly learning and evolving, AI systems can stay one step ahead of cybercriminals, who are always trying to find new ways to exploit vulnerabilities.
4. AI-powered systems can provide real-time detection and response to threats, which is critical in today’s fast-paced world.
5. When used properly, AI and ML can greatly improve an organiSation’s overall security posture.

The Future of AI & ML in Cybersecurity
Artificial Intelligence (AI) and Machine Learning (ML) are transforming how businesses operate and communicate. Its advantages are numerous, with one of the most significant being its potential to transform cyber security.

Also Read | How Artificial Intelligence is upending businesses with Smart Assistants

The future of AI and machine learning in cybersecurity appears bright. Both technologies have the potential to transform the way we protect and defend against cyber threats.

AI can assist in better understanding and forecasting cyber attacks as well as identifying new vulnerabilities before they are exploited. ML can then be used to respond to attacks automatically, stopping them before any damage is done.

These two technologies, when combined, have the potential to significantly improve our cybersecurity posture, making it much more difficult for attackers to succeed.

Industry Perspective:
CDR Praveen Kumar, CISO, ZEE – Technology & Innovation said “AI/ML has become the buzzword in today’s digital world. Due to the humongous amount of data being produced because of rapid digitisation, traditional computation techniques fail to address the problem of generating analytical insights. Therefore, today, AI/ML practices are being introduced into various computational workflows, to leverage the power of Data. To build a successful AI/ML practice, especially in the cybersecurity domain, one needs to have a complete understanding of the “First Principals of cybersecurity”.

Basil Dange, Chief Information Security Officer, Aditya Birla Sun Life AMC Limited said, “With the introduction of AI/ML with most of the security solutions, the admin can take faster decisions by identifying the behaviour of the attacker and blocking/remediating the same such capabilities are enabled for solutions deployed on an endpoint to permitter to the cloud. Correlation is done much faster with AI/ML to my knowledge and review still there are manual inputs/efforts required from the security team to enhance the capabilities further”.

Niranjan Reddy, Infra Head & Chief Information Security Officer, Polycab India Limited said, “Solutions enhanced with AI/ML simplify the protection of vulnerable data even in the most crucial and complex situations. It helps in personalizing the mitigation approach depending on the business requirements, real-time threat identification, analysis, and prevention. An AI-based cybersecurity system relies on the continuous data flow to filter patterns and backtracks the attacks for smarter protection. AI/ML-based solutions help organisations to protect their environment even before the attack has happened through predictive analytics”.

Lalit Trivedi, Head IT & Chief Information Security Officer (CISO), ITI Asset Management Limited said “AI is becoming increasingly important in cybersecurity. It can help analysts detect and respond to threats much more quickly and effectively. By using machine learning algorithms, AI can constantly learn and adapt to new threats. Moreover, it acts as a powerful tool in the fight against cybercrime”.

Conclusion
However, there are also some challenges that need to be addressed before AI and ML can truly reach their potential in cybersecurity. Firstly, both technologies require a lot of data in order to be effective. This data needs to be properly labelled and structured in order for the algorithms to learn from it effectively.

Secondly, AI and ML models need to be constantly updated as new threats emerge. This can be a challenge, as it requires significant investment in both time and resources.

Finally, there is always the risk that cyber attackers will find ways to circumvent these technologies. As such, it is important to have other layers of defence in place as well.

Despite these challenges, the future of AI and ML in cybersecurity looks very bright. These technologies have the potential to transform the way we protect our online assets and defend against cyber threats.