The AI Exchange: Innovators in Payment Security Featuring IBM
Welcome to the PCI Security Standards Council’s blog series, The AI Exchange: Innovators in Payment Security. This special, ongoing feature of our PCI Perspectives blog offers a resource for payment security industry stakeholders to exchange information about how they are adopting and implementing artificial intelligence (AI) into their organizations.
In this edition of The AI Exchange, IBM Principal Product Manager, Financial Services, IBM Infrastructure, Sana Samawi, offers insight into how her company is using AI, and how this rapidly growing technology is shaping the future of payment security.
How have you most recently incorporated artificial intelligence within your organization?
IBM offers data platforms and AI to help enterprises build, deploy, and govern AI models at scale. IBM also applies AI internally to streamline and automate HR, IT Support, and in its products and consulting services. We are actively working with industry and ecosystem partners to on-board third-party AI applications, agents, models and workflows, focusing on secure AI deployment, governance, and risk management for regulated industries.
What is the most significant change you’ve seen in your organization since AI-use has become so much more prevalent?
The biggest change has been the shift from experimentation and pilot projects to enterprise-wide AI deployments, with greater focus on governance, security, compliance, and measurable business outcomes. AI use is not just within applications, but it is also being embedded deeply into the hybrid infrastructure with on-chip AI accelerators, use of AI for scheduling workloads, and with AIOps.
How do you see AI evolving or impacting payment security in the future?
AI plays an increasingly important role in fraud detection and prevention using anomaly identification, real-time threat analysis, behavioral biometrics for identity and authentication, and automated compliance monitoring in helping organizations strengthen payment security while improving operational efficiency. Agentic AI operating in-line with transaction flows holds out the promise to go beyond detecting fraud to preempting it by blocking fraudulent transactions without human intervention. Trust as-a-Service (with HSMs providing the chain of trust and cryptographic boundary control), federated learning, and privacy-preserving AI will enable payment ecosystem participants to effectively collaborate to address fraud which in essence is a network problem.
What potential risks should organizations consider as AI becomes more integrated into payment security?
Organizations should address risks related to data privacy, model bias, adversarial attacks and stronger boundary protection, regulatory compliance, explainability, and ensuring appropriate controls around access to sensitive payment data. With Agentic AI in payments, businesses will have to factor the potential of cascading and compounding failures as they can propagate fast through the network and address business control issues such as accountability as agents make autonomous decisions.
What advice would you provide for an organization just starting their journey into using AI?
Start with clear business objectives and use-case-first thinking, get your data in order with focus on trusted data (AI is only as good as your data), build for trust, governance and security from day one, and optimize AI usage to those priorities that drive most value such as operational cost and efficiency, product/service innovations, and innovative business models.
What AI trend (not limited to payments) are you most excited about?
I am most excited about AI at scale for regulated industries – agentic AI combined with confidential compute, regulated industry frameworks, and enterprise governance, enabling automation of complex processes while maintaining trust, security, and oversight.
About Author
