Cybercrime is surging due to the interconnectedness of digital technologies and the rise of sophisticated cyber-attacks has highlighted the need for advanced security measures that can keep up with these threats. According to recent statistics, 36% of malware instances were delivered via email, showcasing the pervasive nature of cyber threats. Moreover, between 2021 and 2023, there was a staggering 73% increase in data breaches, with the average cost of a data breach reaching up to $4.45 million.
Based on a survey by Cisco, 91% of companies are increasing their cybersecurity spending substantially due to increased risk because of digitization, growth in different types of threats and attacks, financial impact and increasing sophistication of attacks.
These numbers illustrate that traditional cybersecurity methods are no longer sufficient. This article will explain why it’s becoming increasingly vital to integrate Artificial Intelligence (AI) to enhance cybersecurity.
Threat Detection: Identifying and responding to threats in real-time
AI’s most significant contribution to cybersecurity is its ability to detect threats in real-time. Traditional security systems often rely on intrusion detection systems and its foundational method called signature-based detection. This method can monitor and analyze network traffic and can identify malicious activity. However, with the rapid evolution of cyber threats, more is needed.
AI, on the other hand, uses machine learning algorithms to analyze patterns to detect anomalies that may indicate a cyber attack, which is also the need of the hour.
For instance, in 2023, Darktrace, a cybersecurity firm employing AI to monitor network traffic and identify unusual activity, reported that its AI system had detected and mitigated a ransomware attack on a major SaaS environment, preventing significant data loss and financial damage.
Dr. Eric Cole, a cybersecurity expert and former CIA analyst, in a webinar with Abnormal Security said, “Our goal in living is not to say we’re never going to get sick.” He continued, “Our goal is to minimize the frequency with which we get sick and the impact it has on our lives.” Similarly, “Cybersecurity is not about preventing all attacks. Cybersecurity is all about timely detection and minimizing damage.”
Behavioral analytics: Monitoring user behavior to detect anomalies
Cybersecurity is getting a new life with AI’s ability to perform behavioral analytics. By continuously monitoring user behavior, AI can establish a baseline of normal activity and detect deviations that may indicate a security breach. This approach is particularly effective in identifying insider threats, where an employee’s actions may compromise security.
A study by the Ponemon Institute found that insider threats accounted for 31% of all data breaches in 2023. AI-driven behavioral analytics can detect unusual access patterns, such as an employee accessing sensitive data outside of regular working hours or from an unusual location. Or, an intruder’s undetected presence on a network over a long period of time to steal sensitive data aka advanced persistent threats (APTs).
By identifying these anomalies, organizations can take proactive measures to prevent data breaches and also on average can save up to US$1.76 million by using security AI and automation than companies that don’t use AI.
Fraud prevention: Detecting and preventing fraudulent activities
AI’s ability to analyze large datasets and identify patterns makes it an invaluable tool in fraud prevention. Financial institutions, in particular, are leveraging AI to detect and prevent fraudulent activities. By analyzing transaction data and identifying anomalies, AI systems can detect fraudulent transactions in real-time and prevent financial losses.
A report by Juniper Research estimated that AI-powered identity verification to prevent fraud could save financial institutions US$900 million by 2028 and reduce the average time for digital onboarding from over 11 minutes in 2023, to under 8 minutes in 2028. These systems use machine learning algorithms to analyze transaction data and identify patterns that indicate fraud, allowing institutions to take swift action to prevent losses.
Automated response: Swiftly addressing security breaches with minimal human intervention
One of the most significant benefits of AI in cybersecurity is its ability to automate responses to security breaches. Traditional methods often require human intervention, which can be slow and prone to error. AI-powered systems, however, can swiftly address security incidents with minimal human intervention, reducing response times and mitigating damage as well.
For example, IBM’s QRadar Suite uses AI to automate threat detection and response. Such software can cater to different industries like finance, healthcare, entertainment, educational institutions and more by detecting attacks on an IT system automatically. Once it detects a threat, this software isolates the affected endpoint from the network, terminating malicious processes, or alerts security teams for further investigation.
Future potential of AI and their implications for cybersecurity
The potential of AI in cybersecurity is vast and evolving. With advancements in AI technologies from detection to automated response, fighting cybercrime is becoming increasingly effective.
For instance, AI and edge computing can work together to provide real-time threat detection and response at the network edge, reducing latency and improving security.
However, the widespread adoption of AI in cybersecurity also presents challenges, such as the need for extensive infrastructure upgrades, addressing cybersecurity concerns and ensuring equitable access. But in 2025, its still dawn for AI in cybersecurity and hopefully with time we will see much more innovation in cybersecurity.
