Agentic AI Poised to Pioneer the Future of Cybersecurity in the BFSI Sector By: Dipesh Kaura, Country Director – India & SAARC, Securonix

We have almost forgotten the days when we had to physically drive to the bank and spend considerable time in queues, manually filling forms for even simple transactions. Today, we can access several banking services on the web from the comfort of our homes.  Internet banking has long proven to be a game changer, but also a prime target for cybercriminals, where data breaches are a major concern.  The cost of a data breach in the financial services sector is significantly higher as compared to the global average cost in general.  Threats from stolen credentials, system intrusions, supply chain breaches, and misconfigured cloud environments are some of the risks faced by financial institutions.

Shift from Traditional to Proactive Cybersecurity Defense

Traditionally, financial institutions relied on firewalls, intrusion detection systems, anti-virus solutions, intrusion prevention systems, and malware sandboxes that were reactive and relied on static rule sets, signatures, and patterns of attacks to mitigate them.  They were effective against known threats but failed in the presence of modern-day, sophisticated, and evolving ones. 

Furthermore, traditional AI solutions are task-specific, reactive, and function by leveraging pre-defined rules and machine learning algorithms.  These solutions are used to detect fraud based on historical data patterns, but are not capable of dynamically adapting to real-time changes without human intervention and manual retraining. Responding to evolving threat patterns is a great challenge for these conventional solutions.

Enter agentic AI systems that represent a network of intelligent agents having the capability for independent decision-making and adaptive learning.  This extends the capabilities of traditional AI systems by incorporating autonomous decision-making and execution, while adopting proactive security measures.  It is poised to revolutionize cybersecurity in the banking and financial services sector while bridging the gap between the speed of cyber-attacks and the slow, human-driven incident response. 

Applications of agentic AI in Cybersecurity

·      Real-time threat detection and response

It is certainly a challenge for human analysts to sort thousands of alerts, many of which are false, leading to missing a real threat and detecting it after the damage occurs. With the capability to differentiate between false and real potential threats, agentic AI autonomously identifies, triages, and mitigates threats in real time. Agentic AI systems also act as intelligent cybersecurity agents by monitoring for anomalies and classifying risk levels.  It also continuously refines its threat detection capabilities based on the threat patterns that are ever-evolving.

·      Proactive and adaptive threat hunting

Agentic AI will proactively and autonomously hunt for threats across the IT systems within the financial institution by actively looking for vulnerabilities and possible threat vectors before they are exploited by threat actors.  Agentic AI systems leverage their capabilities in simulation, where potential attack scenarios are modeled to identify vulnerabilities in the security posture.  Data from logs, network traffic, and activities from endpoints are correlated to spot attack vectors as a part of the threat hunting process.  Agentic AI also learns from new attack techniques and further refines its ability to proactively hunt for threats.

·      Enhances identity and access management (IAM)

Smart self-learning AI agents make real-time decisions and can be leveraged to enhance IAM solutions.  These AI agents assist in managing quick and accurate access and make IAM systems more scalable and efficient, while strengthening security and significantly reducing manual tasks.  Agentic AI systems improve security measures by continuously monitoring user behavior.  In the case of a user accessing sensitive data from an unauthorized device, agentic AI triggers further verification of identity, such as biometric checks or passcodes.  Identity Governance is enhanced where agentic AI uses advanced analytics to pre-analyze access patterns and identify high-risk permissions.

Building agentic AI for Financial Institutions

1.    Define the objectives and use cases

Organizations have to identify all challenges and opportunities where agentic AI can be implemented.  It can help to reduce false positives and establish a high fraud-detection rate, optimize portfolios, and enable credit scoring, among other benefits.  All use cases must be prioritized based on ROI and the availability of data sources.

2.    Choose the Right Technology and Tools

It is crucial to use the right tools and platforms while building agentic AI.  They should have LLM, API, integration, scalability, and security capabilities. These agentic AI tools should provide end-to-end encryption, strict access control, and be able to minimize data breaches, among other security functions. Leading tools include Google Cloud AI Platform, Microsoft Azure Machine Learning, and Amazon Sagemaker. ReliaQuest-Greymatter for unified threat detection, investigation, and response, Vectra AI platform for network detection and response, Picus Security Platform validates, prioritizes, and strengthens defenses, and Anomali Threat Intelligence Platform, for threat intelligence ingestion, detection, are some of the key agentic AI tools used in cybersecurity.

3.    Establish Governance Framework

AI governance policies have to be built in alignment with global and local regulatory requirements.  Explainability, accountability, and human-in-the-loop mechanisms have to be defined well.  All automated decisions have to be regularly updated.

4.    Integration with Existing Systems and Infrastructure

AI agents have to be deployed into both customer-facing for better customer experience as well as internal systems.  By establishing an agentic AI ecosystem, agents can collaborate across functions. Risk management, compliance monitoring, operational efficiency and fraud detection functions can be streamlined too.

Furthermore, the performance of agentic AI systems has to be regularly monitored, evaluated, and updated to adapt to evolving conditions.

With traditional defense systems struggling to keep pace with modern-day sophisticated cyber threats and limited human resources, agentic AI systems are gaining traction.  As digital finance is becoming the mainstay of economies across the globe, these systems will become an indispensable tool for security teams to enhance their organization’s cyber resilience.