As much as the Internet has become a part of our lives, it has come with its own set of challenges. With the introduction of new technology, existing systems become obsolete, we need to adapt to changing times. The world has now become more interconnected through the Internet but associated risks, too, have doubled. More and more organizations are becoming vulnerable to surface attacks as it keeps evolving at a greater speed. Big or small, every organisation is susceptible to threats. There are around hundreds and billions of data that need to be analyzed to calculate the actual risk for an organisation.
According to a Norton report, the global cost of typical data breach recovery is $3.86 million. The report also indicated that companies need 196 days on average to recover from any data breach. As threat actors rush to exploit the situation, analyzing and improving cybersecurity measures has become quite difficult for humans alone.
This is why Artificial Intelligence (AI) and Machine Learning (ML) have become potent tools to counter improve the security posture efficiently. AI and ML hold the capacity to analyze millions of data in one go and identify different types of threats as well. New methodologies are being devised to make cyberspace risk-free. Threats can range from malware exploiting a zero-day vulnerability to identifying risky behavior, to a phishing attack or anybody wanting to download malicious code.
AI and ML keep evolving with time, they take a cue from the past to identify new types of attacks. Histories of behavior build profiles on users, assets, and networks, allowing AI to detect and respond to deviations from established norms.
Cybersecurity providers introduce a continuous process of measures to prevent or remediate cyber threats and cybercriminals’ methods to solve security measures. But as much AI and ML make things convenient for us, the hackers, too, orchestrate newer forms of threats. This is a never-ending cycle.
AI, therefore, is now emerged as a critical tool for cybersecurity companies who want to stay ahead. Cybersecurity involves a lot of data points and this is where AI comes into the picture. The latter helps in data clustering, classification, processing, filtering, and management. It helps build defensive measures stronger and response times faster. Yes, AI is a very strong concept but cannot be said to be a perfect solution as it cannot run on its own. It needs data chunks based on which it makes decisions. AI, therefore, can never replace human intelligence, especially when identifying and mitigating threats. Still, it does perform the role of advancing cybersecurity in powerful ways.
Advantages of AI in cybersecurity
AI has successfully proven itself in different areas, and cybersecurity is among the top. As cyberattacks evolve continuously with the rising use of multiple devices/technologies, AI and ML can help fight against cybercriminals, automate threat detection, and respond more effectively than conventional software-driven or manual techniques.
- Threat detection
AI can be used to detect cyberthreats and possibly malicious activities, too. For the identification of threats, traditional security techniques use signatures or indicators. Such an approach turns effective for previously encountered threats, but they are not helpful for threats that have not been discovered yet.
Signature-based techniques can successfully detect about 90% of the threats. Replacing traditional methods with AI can increase the detection rates up to 95%, but there might be chances of getting an explosion of false positives. The best way would be to combine both traditional methods and AI. This can, thus, result in a 100% detection rate and minimize false positives.
Companies can also use AI to enhance the threat hunting process by integrating behavioral analysis. For example, leveraging AI models for developing profiles of every application within an organization’s network by processing high volumes of endpoint data.
- Handling bots
Bots form a major part of internet traffic today, which can be dangerous. Bots can perform account takeovers with stolen credentials or create false accounts or fraudulent data.
It is not possible to tackle automated threats with manual responses. With AI and ML, it becomes easier to build a thorough understanding of website traffic and differentiate between good bots (e.g., Search engine crawlers), bad bots, and humans.
With AI, cybersecurity teams can analyze a vast amount of data and adapt strategies to alter the landscape continually.
Chief Technical Architect and Head of Data Science at Netacea Mark Green Wood explains: “By looking at behavioral patterns, businesses will get answers to the questions ‘what does an average user journey look like’ and ‘what does a risky, unusual journey look like.’ From here, we can unpick the intent of their website traffic, getting and staying ahead of the bad bots.”
- Predicting breach risk
With AI systems, it is possible to determine the IT asset inventory. An IT asset inventory is an accurate and detailed report of all users, devices, and applications with different access levels to multiple systems.
Considering the asset inventory and threat exposure, AI-based systems can easily predict how and where data breaches can occur. Accordingly, the organizations can plan and allocate resources towards the areas that are most vulnerable.
Prescriptive insights from AI-based analysis permit configuration and improve controls and processes to reinforce cyber resilience.
- Strong endpoint protection
The number of connected devices being used for remote working has increased rapidly. It is important to secure these devices, and AI can help do it faster and efficiently.
Antivirus software and VPNs (Virtual Private Networks) can help protect against remote malware and ransomware attacks, but they often work on endpoint protection standard processes. It implies that to stay protected against any latest threats, it is essential to keep up with standard procedures.
Suppose a failure in updating the antivirus solution occurs or no information is received from the vendor about the same. In that case, chances are high that the system may get infected by the virus. So, in this case, if a new type of malware attack occurs, signature protection may not be able to protect against it.
Tim Brown, VP of Security Architecture at SolarWinds, explains: “AI-driven endpoint protection takes a different tack, by establishing a baseline of behavior for the endpoint through a repeated training process. If something out of the ordinary occurs, AI can flag it and take action — whether that’s sending a notification to a technician or even reverting to a safe state after a ransomware attack. This provides proactive protection against threats, rather than waiting for signature updates.”
The above scenario shows that AI is fast emerging and is a must-have technology for Information Technology (IT) and security teams to enhance their performance. Humans are no longer completely capable of securing an enterprise-level attack surface. AI gives security professionals the much-needed analysis and threat identification to minimize breach risk and enhance security posture.
Moreover, it becomes easier to help discover and prioritize risks with AI, it generates direct incident reactions, and checks upon malware attacks before they are even detected.
Thus, AI will drive cybersecurity forward and help organizations create a more robust security posture with all of its potential.
To know more visit our latest whitepapers on AI and cybersecurity threats here.