Cybersecurity has come a long way in its journey. Back then, it used to be about offering basic defenses against simple viruses and computer hacks. Defense methods were pretty straightforward, like installing antivirus software and setting up firewalls to secure a network.
However, as technology has evolved, so did the threats with it. Cyber attacks have now become more complex and more frequent, which is a serious challenge to present-day security networks as well as system managers. This change demanded a more stringent approach to protecting sensitive networks from online threats and that’s where Artificial Intelligence(AI) steps in.
This article shares details of how AI is transforming the way of tackling cyber attacks by offering smarter and faster threat defense mechanisms.
Current Scenario of Cyber Attacks
Accenture’s 2023 State of Cybersecurity Report found that 68% of organizations had experienced a cyberattack in the past year. Similarly, IBM’s 2023 Cost of a Data Breach Report states that the average cost of a data breach in 2023 was $4.35 million – a 12% increase from the last year. The report also found that the time taken to identify and resolve a data breach event is 277 days on average.
The essence of the above and many such reports is that there’s an alarming increase in sophisticated cyber-attacks and their frequency. To which the answer is AI-powered Cybersecurity Systems designed to offer robust threat detection against all kinds of cyberattacks.
How does AI in Cybersecurity work?
AI algorithms play a crucial role in cybersecurity by providing cognitive abilities to software programs and devices. This enables the devices to learn from past data and recognize cyber-attacks when they arise in the future.
AI algorithms analyze vast amounts of data fed to them to identify patterns and make decisions to resolve cyber attacks that have already happened and prevent cyber attacks from happening in the future.
Let’s also talk a bit more about AI. The AI concept is built on three key ideas: AI itself, Machine Learning (ML), and Deep Learning (DL).
AI is the big picture – it’s about creating smart machines that can think and react like us, humans. Next is Machine Learning which is a part of AI that allows machines to learn from experience or the vast amount of data sets provided to the algos. The final is Deep Learning which goes even deeper; it’s a type or a subset of ML that tries to function like our brains. It uses something called neural networks to process information in complex layers to make human-like decisions.
Advantages of AI in Cybersecurity
The following are the advantages that AI brings to the table when incorporated into traditional cybersecurity systems:
- Speedy 24X7 Threat Detection
AI can analyze massive amounts of data in a very short amount of time. This means it can spot potential threats, like malware or suspicious activities, almost instantly. Besides, AI systems don’t sleep or take breaks like humans. So they can monitor networks around the clock, ensuring continuous threat protection.
- Precise Threat Recognition
AI is excellent at identifying patterns due to which it’s able to precisely recognize the unusual behavior of users and systems. In case of an unusual activity, like an unknown user accessing sensitive data, AI can quickly mark the activity as a potential data breach and alert the system managers.
- Automated Response
Once a threat is detected, AI can act swiftly to resolve the attack. It can isolate the infected system from the rest of the network to prevent the spread of malware. With AI-automated handling of threat detection and its resolution, cybersecurity professionals can focus on other important security challenges at their disposal.
- Predictive Analysis
AI doesn’t just react to threats, but it can also work proactively to predict the threats in advance. By analyzing past data, the latest trends, and network traffic, AI can spot unusual activity such as a sudden increase in traffic or access requests from unknown locations that might be an indication of a cyberattack.
AI in Cybersecurity into Action – Real-world examples
Here are some real-world stories of organizations that have successfully implemented AI into their cybersecurity strategies:
- Darktrace
Darktrace is an AI-powered cybersecurity firm that uses machine learning to identify and respond to threats in real-time.
One of their notable success stories is when they detected and prevented a ransomware attack on a large manufacturing firm. Darktrace’s AI identified suspicious activity on the company’s network and automatically isolated the affected systems thereby preventing the babuk ransomware from spreading further and causing damage to other parts of the network.
- Cloudflare
Cloudflare is a cloud-based cybersecurity company that uses AI to protect its customers from a wide range of threats like DDoS attacks, phishing, and malware infections.
In 2016, Cloudflare used its AI to mitigate a massive DDoS attack that targeted Dyn, a major internet service provider headquartered in Manchester. The attack was one of the largest ever recorded DDoS attacks in history– a one terabit per second traffic flood. However, Cloudflare’s AI was able to absorb the majority of the traffic and prevented the attack from disrupting online services for millions of people.
- IBM
IBM Watson for Cybersecurity– is an AI platform developed by IBM that helps organizations to automate and improve their cybersecurity systems.
One of their customers, a large financial institution, used Watson to identify vulnerabilities in their IT infrastructure. Watson was able to scan millions of lines of code and then identified 100,000+ vulnerabilities in the network, many of which were previously unknown to the company. The firm then amended these vulnerabilities before they could be exploited by hackers.
Challenges and Limitations of AI-Enhanced Cybersecurity
While AI has proved itself to be a game-changer in the cybersecurity realm, it also brings several challenges with it.
- Complex Implementation
AI systems depend heavily on large volumes of data for their training. In cybersecurity, this means AI needs vast datasets of both secure and malicious activities to accurately distinguish between the two. However, obtaining comprehensive and up-to-date datasets is a difficult affair, as cyber threats constantly evolve with time. This limits the AI’s capability to recognize and respond to cyber threats of the future.
Besides, extensive data training requires expertise and resources that many organizations can’t afford.
- AI-Driven Cyber Attacks
You’re not alone as cybercriminals are using AI too, making cyber warfare far more challenging than ever.
An example of an AI-driven cybercrime is in 2018 when hackers used AI to carry out a series of spear-phishing attacks targeting cryptocurrency exchanges. They used AI to create fake LinkedIn profiles of employees of the crypto exchanges. Then they used these fake profiles to connect with genuine employees of the exchange and sent them emails containing malicious links. They were able to steal cryptocurrencies worth millions of dollars as a result of this phishing attack.
- False Positives and Negatives
AI systems, particularly those built through machine learning, can generate false positives and negatives sometimes. They might suggest a safe activity as a threat or do the opposite. These inaccuracies will not only lead to the wastage of time and resources if you later realize in case of a falsely generated signal but can also cause more damage to a system in case of a real cyberattack.
Conclusion
AI has revolutionized the cybersecurity landscape by offering precise and predictive threat detection mechanisms. However, there are also several red flags emanating from AI–. AI can sometimes produce false results and there’s also a risk of AI-driven cyberattacks. In addition, incorporating AI in cybersecurity systems is a complex and costly affair.
Despite these odds, the benefits that AI brings to the table can’t be ignored due to its effectiveness in tackling ever-evolving cyberattacks.
