Artificial intelligence (AI) is reshaping most industries, with cybersecurity being one of them. Utilising AI in cybersecurity will help businesses with the ability to create content, process data tailor user experience as well as protect against data breaches and other internet-based threats.
Data breaches affect millions of individuals each year which makes the need for more robust cybersecurity measures. By employing AI in cybersecurity practices, businesses can not only prevent a significant number of these attacks but also mitigate the damage in the event of a successful breach.
To get the most out of AI in security, businesses should understand the relevant tools needed, but also the limitations involved. In our latest blog, we shed light on the use of AI in cybersecurity and how the two can work together.
Understanding Cybersecurity and Its Importance
At its core, cybersecurity involves safeguarding digital assets against unauthorised access, breaches, and attacks. It detects real-time threat detection, offers malware programs, and enforces strong passwords. It is important to have regular system updates to eliminate vulnerabilities.
Another integral area of cybersecurity is employee training. Each employee who interacts with business digital assets should have relevant knowledge about the most common cyber attacks including phishing, malware and email attacks. Employees should be able to determine the signs of ransomware attacks with the right cyber security awareness training.
The Role of AI in Cybersecurity
AI is a transformative technology that allows machine learning to carry out various tasks that are typically done by humans. AI in cybersecurity is now integral and it is essential to understand the broader purpose.
Understanding how AI is utilised in cybersecurity empowers businesses to effectively leverage these tools, enhancing system security while maintaining optimal performance. Some of the roles of AI in cybersecurity include:
AI in Threat Modeling:
Threat modelling involves identifying potential threats and formulating strategies to counter them. AI excels in this area by swiftly scanning vast amounts of data and pinpointing actions that may pose a threat, which human operators might overlook. The speed and accuracy of AI-driven threat modelling limit the damage that cyberattacks can inflict, as threats are eliminated before they can access critical data. Additionally, AI in cybersecurity demonstrates proficiency in identifying false positives, a challenge even for cybersecurity experts. By minimising false positives, AI enables organisations to focus their attention on genuine threats efficiently.
AI in Pattern Recognition:
AI’s ability to monitor large datasets allows it to discern patterns in user behaviour, enabling it to take proactive measures. Approximately 36 per cent of data breaches in companies with more than 1,000 employees and 44 per cent in smaller organisations result from internal threats caused by malicious employees. AI in cybersecurity can prevent such breaches by flagging suspicious behaviour, and preventing unauthorised access based on potential malicious intent. Continuous monitoring of employee activities enables AI to detect anomalies such as unusual mouse activity or file movements, leading to prompt identification of hacked accounts and breach prevention.
AI in Password Management:
Weak or stolen passwords are responsible for a staggering 80 per cent of breaches, undermining even the most robust cybersecurity measures. To counter this, AI-based password selection programs have emerged, requiring users to create strong passwords that meet specific criteria, such as incorporating capital letters, numbers, and symbols. These programs help businesses fortify their defence against unauthorised access.
AI and Continuous Monitoring:
Cyber threats can occur at any time, necessitating constant vigilance. AI’s tireless nature ensures round-the-clock monitoring and timely mitigation of potential threats. By alerting users to potential breaches in real-time, AI in cybersecurity helps organisations stay proactive and responsive.
AI-Powered Incident Response and Recovery:
AI-driven tools swiftly detect potential breaches, minimising the damage that hackers can inflict. They can also aid in planning incident response and facilitating digital asset recovery. Recommendations for preventing future attacks can be provided once the recovery process concludes.
Benefits and Limitations of AI in Cybersecurity
The integration of AI in cybersecurity offers many benefits such as allowing businesses to defend against increasingly sophisticated attacks as well as anticipate other vulnerabilities that can arise in the future. When employed effectively, AI empowers businesses to stay one step ahead of cybercriminals. Artificial intelligence (AI) offers several benefits in the field of cybersecurity.
Automated Threat Detection & Incident Response
Artificial intelligence has been developed with the aim of emulating human intelligence but at a more sophisticated level. As a result, it possesses the ability to analyze vast amounts of data with greater speed and effectiveness compared to cyber security professionals. In the realm of threat detection, AI in cybersecurity has systems that can be utilized to autonomously identify anomalies that arise within computer systems.
Reduce False Positives & False Negatives
In cybersecurity, a false positive is a false flagging of harmless files or activities as malicious attacks. A false negative is just the opposite of this where there is no flag when there is an actual attack. These situations can arise when using rule-based systems that struggle to accurately differentiate between benign and malicious activities. Both scenarios pose significant risks to businesses, prompting the intervention of artificial intelligence.
AI in cybersecurity systems can help to outperform humans in effectively fine-tuning their own rules and configurations. This in turn minimises the risk of getting these results.
AI Can Be Tricked
Adversarial attacks pose a challenge for AI-based security systems. These attacks involve the intentional manipulation of data by an attacker to deceive machine learning algorithms. One instance of such an attack is the creation of a counterfeit website that closely resembles a legitimate one, successfully misleading the AI in cybersecurity security systems into perceiving it as authentic.
AI Can Be Biassed
It is crucial to acknowledge that AI systems can exhibit bias. This indicates that the outcomes produced by these systems may be influenced by the biases inherent in the training data employed. If the training data itself carries biases, the AI system is likely to reflect those biases.
While AI in cybersecurity has many benefits and is significantly advancing the industry, it is essential for businesses to understand that there are limitations to these AI tools. AI in cybersecurity, aligned with a team of experts will in turn be a better option for your business. Contact us today and speak to our cybersecurity consultancy who will be able to offer expert advice.