Financial institutions are confronting a rapidly evolving threat landscape where artificial intelligence is weaponized for sophisticated cyberattacks. In response, technology firm Hexaware is deploying an AI-first strategy designed to bolster banking defenses, moving beyond traditional security protocols to offer a more dynamic and predictive form of protection. This approach centers on preemptively identifying and neutralizing threats that are becoming increasingly difficult to detect.
The core of this strategy is a proprietary “Trust Framework” that leverages behavioral analytics to understand and monitor user actions. With malicious actors using AI to impersonate legitimate customers and transactions, the framework’s ability to establish personalized baselines of normal activity allows it to flag subtle deviations that could signal a security breach. This shift from reactive measures to a proactive, AI-driven defense system aims to address the significant automation gains and enhanced precision that criminals now possess.
The Evolving Threat Landscape
The advent of accessible generative AI has fundamentally transformed cybercrime, empowering attackers to automate and scale malicious campaigns with unprecedented ease and sophistication. Malicious actors now use advanced algorithms and machine learning to bypass conventional security measures, creating convincing phishing emails, deepfake scams, and banking Trojans designed to steal credentials and financial data. A report noted that AI has “radically transformed the cybercrime landscape, simplifying attack processes and enabling the most inexperienced cybercriminals to carry out large-scale malicious actions with greater ease and precision”.
Statistics highlight the severity of this new reality. In the second half of 2024, credential phishing attacks saw a staggering 703% increase. Furthermore, deepfakes now account for 6.5% of all fraud attacks, a dramatic rise from previous years, with 53% of financial professionals reporting they have encountered attempted deepfake scams. Attackers are leveraging generative AI to compose phishing emails up to 40% faster, and more than 82% of such emails now incorporate AI technology in some form. This has forced a strategic shift in the financial sector, where data breaches can lead to devastating financial loss, reputational damage, and severe regulatory penalties.
A Proactive Defense Framework
To counter these AI-powered threats, Hexaware developed its Trust Framework, a system designed for proactive and intelligent threat detection. Unlike traditional, rule-based security systems that primarily react to known threats, this framework focuses on understanding the nuances of individual user behavior. By continuously monitoring activity, the system creates a personalized behavioral baseline for each user within a banking network. This allows it to identify anomalies that might otherwise appear benign.
This method is part of a broader cybersecurity strategy known as User Behavior Analytics (UBA). UBA works by collecting and analyzing data from various sources, such as log files and network traffic, to establish normal patterns of behavior. When a user’s actions deviate significantly from their established baseline—for instance, by logging in from an unusual location, accessing sensitive data they typically do not use, or transferring large amounts of information—the system flags the activity as a potential threat. This proactive monitoring enables security teams to investigate and mitigate potential breaches before they escalate.
Leveraging Generative AI for Security
Automated Threat Detection and Response
The same generative AI technology used by attackers is being repurposed for defense. AI-driven security platforms are crucial for identifying and responding to threats more quickly. These systems can analyze immense volumes of security data in real time to uncover patterns and anomalies that would be invisible to human analysts or conventional tools. According to industry data, companies using AI-powered security detect threats up to 60% faster than those relying on traditional methods. Generative AI can automate routine incident responses, such as blocking malicious IP addresses or isolating compromised systems, which frees up human cybersecurity professionals to focus on more strategic and complex challenges.
Predictive Risk Assessment
Beyond immediate detection, generative AI enhances cybersecurity by enabling predictive risk assessment. By analyzing historical security data and simulating potential attack scenarios, these AI models can identify vulnerabilities and predict emerging threats before they materialize. This capability allows financial institutions to move from a reactive to a proactive security posture, strengthening their defenses against novel and complex attack vectors like zero-day exploits. The integration of AI into Security Operations Centers (SOCs) has become essential for identifying subtle patterns of malicious activity that might otherwise go undetected.
Implications for the Banking Industry
The adoption of AI-first security strategies is becoming a necessity for survival and compliance in the modern financial industry. With cyber threats growing in volume and sophistication, heightened regulatory requirements demand more robust protection of customer data and assets. The financial sector remains a prime target for cybercriminals due to the vast quantities of sensitive information it handles. As a result, investment in AI for cybersecurity is projected to grow significantly, with one forecast estimating that spending will reach nearly $97 billion by 2027.
By partnering with technology providers like Hexaware, banks can implement advanced defensive systems without needing to develop the capabilities entirely in-house. More than 90% of AI cybersecurity capabilities currently come from third-party tools. This collaboration allows financial institutions to not only defend against current threats but also to adapt to the future landscape of cyber warfare, where the battle will increasingly be fought between competing AI systems.