With the help of artificial intelligence (AI) and machine learning (ML), cybercriminals are creating novel, sophisticated threats more frequently and with fewer resources than ever before. These threats are increasingly difficult to detect using signature-based analysis methods and continue to wreak havoc across the digital business landscape. In 2023, the global average cost of a single data breach was $4.45 million1—a 15% increase over the past three years.

But AI and ML are equally as powerful for cybersecurity professionals in their efforts to defend against advanced threat tactics. In this blog—and in Part 2—we dive into the roles of AI and ML in cybersecurity and explore how these emerging technologies both create opportunities for stronger security and complicate the threat landscape.

The Evolving Landscape of Cybersecurity

Today’s cybersecurity professionals find themselves in a never-ending arms race with threat actors who increasingly use AI and ML to one-up security technologies. Generative AI streamlines software creation and scamming campaigns, allowing cybercriminals to craft extensive attacks with fewer people and less expertise. AI combined with ML enables threat actors to create more successful phishing and social engineering attacks as they can now leverage these technologies to manufacture dangerously realistic deepfakes.

Consider one such incident where cybercriminals tricked an employee at the company Retool into revealing their multi-factor authentication (MFA) code—ultimately exposing 27 cloud customer accounts.2 The attacker penetrated the employee’s account through a spear phishing attack. They then navigated multiple layers of security, called the employee using an AI- powered voice clone of an IT staff member, and asked for the MFA code. This attack is a prime example of how today’s organizations are underprepared and need to advance their threat detection methods by fighting fire with fire.

The Role of AI in Cybersecurity

AI plays an essential role in cybersecurity and threat detection through automated data analysis. Incident response and forensics teams need to analyze enormous amounts of data including logs, network traffic, and user behavior in order to identify threats and indicators of compromise (IOC). Using AI for these jobs not only speeds up the process but also helps to detect patterns that manual analysis might miss.

AI can also be used to analyze data from previous encounters and threat actors to identify trends that may be signals of an attack—allowing security teams to anticipate and proactively mitigate risk. In Cyber Magazine,3 Hitesh Bansal, Wipro's Country Head (UK & Ireland) – Cybersecurity & Risk Services explains, "Advanced AI now leverages existing protection technologies to build a logical layer within models to proactively protect data. For example, this can take the form of blocking traffic at the firewall level, before the threats compromise the boundaries of an organisation.”

Machine Learning’s Contribution to Threat Detection

When combined with ML, AI can do much more than just analyze and report data—it can learn and make informed decisions. For instance, an ML-capable cybersecurity solution learns the patterns of normal behavior for an organization and its users. Empowered with this knowledge, AI can detect when an anomalous activity happens. It then enters the threat-hunting process and closely examines the inconsistency. Depending on the nature of the anomaly, AI takes action, such as creating an exception, shutting down the activity completely, or deferring to a human to make the choice.

Traditional detection methods need human input and rely on known malware signatures— meaning someone somewhere had to be infected first. While this method accurately detects certain threats, it struggles to detect zero-day (never before recorded) malware. SynerComm integrates this traditional vulnerability scanning with ML and automatic discovery in its continuous attack surface management (CASM®) and continuous penetration testing (CPT) solutions. This combination enables you to proactively identify IT infrastructure vulnerabilities and improves your threat detection capabilities. Learn more about CASM® and be prepared for the evolving cyberthreat landscape.

Sources:

1. https://www.ibm.com/reports/data-breach
2. https://securityaffairs.com/150981/hacking/retool-smishing-attack.html
3. https://cybermagazine.com/articles/the-role-of-generative-ai-in-tackling-cyber-threats