Inspira Enterprise, a global cybersecurity leader, warns that it’s time for financial institutions to rethink their cybersecurity approach — shifting from reactive defense to AI-led, predictive, and zero-trust frameworks. SecurityDive spoke to Chetan Jain of Inspira to understand the theme
Securitydive.in Why AI and automation are becoming central to financial cybersecurity?
Chetan Jain : AI and automation are becoming the heart and soul of safeguarding the financial services sector from next-generation AI-led threats, which is fighting AI with AI. Mundane tasks performed in financial institutions are rapidly automated by AI. A system driven by AI can analyze a very large volume of financial transaction data in real-time, identify anomalies, indicators of Compromise (IoC), and backdoors that were difficult for security analysts to perform at scale, thereby demonstrating the AI scale and agility.
An AI-powered Security Operations Center (SOC) can automate the majority of the L1 security operations, bringing in agility, efficiency, and significantly reducing the Mean Time to Respond (MTTR) to cybersecurity incidents.
Securitydive.in: Cybersecurity risk is a daily operational reality for financial institutions. Where do you see AI and automation combined force to fill the gap?
Chetan Jain : Cybersecurity risk management has become a strategic priority for financial institutions to ensure operational continuity and enhance overall business results. With automation and AI at the forefront, financial institutions can proactively identify and manage cybersecurity risks based on past patterns. AI-powered Governance, Risk, and Compliance (GRC) solutions can automate the
workflows and compliance obligations to reduce the risk of regulatory non-compliance.
AI and automation can also help bridge the gap in the third-party relationships of the financial institutions and help proactively identify security non-compliances and manage the risks.
Securitydive.in: AI-driven tools that are increasingly trained off the day-to-day activities of the organization. How do you see the operational growth considering sensitive data in place?
Chetan Jain : The potential for AI-driven tools trained on business-as-usual (BAU) activities and underlying data is immense, as it can lead to substantial operational business growth. For sustained operational growth, there is a need to strike the right balance between AI innovation, data security, and privacy.
AI-driven tools using models trained on sensitive data may expose organizations to privacy risks if not carefully governed, since proper controls are absent. To address this risk, organizations must prioritize red-team testing of their AI-driven tools.
Securitydive.in: Risk and fraud management have always been top priorities for financial institutions. AI refines processes in real time. What are the challenges for the financial sector?
Chetan Jain : The key challenge for financial institutions from AI refinements is maintaining the privacy of confidential data. Other predominant challenges include the biased, drifted, hallucinated, and discretionary results produced due to the AI models. Not all workloads in any financial institution are tech-savvy, next-gen, and AI-driven. There exists significant legacy infrastructure within the financial
services ecosystem that does not natively integrate with AI solutions. Integrating such systems with AI can be a very costly affair.
Securitydive.in: Digital threats are evolving in financial sector. How do CTOs require to address the issue with a different perspective than conventional problems?
Chetan Jain : CTOs in the banks should adopt a tech-driven but strategy-focused perspective to address such issues. The core compliance and check-in-a-box cybersecurity activities should transform into digital trust-driven outcomes.
To enable these perspectives and outcomes, many security functions should
converge, including, but not limited to, zero trust architecture, proactive threat defense, AI SOC initiatives, and vulnerability management, among others. CTOs should liaise with a cross-functional leadership team (e.g., CISO, CIO, and CRO) to drive these initiatives across the organization.
Securitydive.in: Statistics says the financial sector faced a series of unprecedented supply chain attacks, in 2025 including vulnerabilities in third-party providers to reach their primary targets. What should be priority while implementing automation and AI?
Chetan Jain : The top priority for the financial sector should be to move away from the manual, point-in-time Third-Party Risk Management (TPRM) assessments to more AI-driven, real-time, continuous third- party evaluations.
AI tools can analyze humongous data points at scale to assign a real-time risk
score to the third party to enable the financial institutions to take proactive measures and be better prepared for any supply chain attacks. Devise an AI cybersecurity strategy and a policy to govern the AI initiatives and be better prepared to handle any cybersecurity attacks in the future.
