How AI, Authentication and Cybersecurity Work Together

As digital payments become the norm, security concerns continue to rise. In ProgressSoft’s recent social media polls, 66% of respondents identified cybersecurity threats as their primary concern when using digital payment solutions. This growing anxiety is justified: as transactions move faster and farther than ever before, so do the threats that target them.

To meet these challenges, financial institutions must adopt a smarter, layered approach to protection. At the heart of this shift are AI-driven fraud detection systems, multi-factor authentication and encryption technologies working together to build trust in every transaction.

Why Cybersecurity Is the Top Concern in Digital Payments

Cybercrime is evolving. From phishing to credential stuffing to ransomware, bad actors are becoming more organized and technologically advanced (Europol, 2024). As the financial ecosystem grows more complex, so do the vulnerabilities within it.

The public is clearly aware. Cybersecurity was selected by 66% of ProgressSoft’s poll participants as the number one concern with digital payment systems. Institutions must now prioritize security not only to remain compliant but also to maintain customer trust.

AI as a Game-Changer in Fraud Detection

AI is no longer a futuristic idea. It is actively transforming the way we detect and prevent financial fraud. In ProgressSoft’s polls, 46% of respondents identified fraud detection as the most promising AI use case for 2025.

What makes AI so powerful is its ability to analyze huge volumes of transactional data in real time. It identifies behavioral patterns, flags anomalies and detects suspicious activity as it happens. Unlike traditional rule-based systems, AI learns and adapts, improving over time to stay ahead of fraudsters.

Predictive analytics takes this one step further. By forecasting potential fraud before it occurs, AI allows institutions to move from reactive defense to proactive prevention. This reduces losses, improves customer experience and minimizes false positives (Fraud, 2023).

Building a Layered Security Approach with AI and MFA

Security is most effective when it’s layered. While AI is powerful on its own, it is even stronger when combined with multi-factor authentication (MFA) and encryption.

ProgressSoft’s poll respondents agree. When asked about the most significant measure for enhancing payment security, 40% chose MFA, reflecting a growing consensus that password-based access alone is no longer sufficient.

AI complements MFA by monitoring behavior and transaction integrity after a user is authenticated. Encryption ensures that sensitive data remains protected throughout the transaction. Together, these tools form a layered defense system resilient against evolving threats (Kaspersky, 2024).

Best Practices for Securing Digital Payments in 2025

To fully realize the benefits of AI-driven fraud detection, financial institutions must follow strategic best practices:

• Combine technologies: Use AI alongside biometric authentication, MFA and tokenization for end-to-end security
• Educate users: Build awareness among customers and staff about security best practices
• Ensure compliance: Align AI and encryption strategies with evolving regulatory standards
• Keep AI models updated: Regularly retrain models and monitor for drift to stay ahead of evolving fraud tactics

Security in Layers: No One-Size-Fits-All

In today’s complex threat landscape, a single line of defense is no longer sufficient. Security must be approached in layers by combining perimeter defenses, identity management, anomaly detection, encryption, endpoint protection and continuous monitoring.

This “defense-in-depth” strategy ensures that if one layer is breached, others can still prevent or contain the attack. There is no one-size-fits-all approach; each institution must tailor its security stack based on risk profiles, data sensitivity and operational realities. AI-powered tools can help orchestrate these layers more intelligently, adapting in real time to emerging threats (Naser, 2025).

By adopting these steps, organizations can build a security framework that is both adaptive and future-ready.

Toward Smarter, Adaptive and Resilient Payment Security

As digital payment ecosystems expand, the future of cybersecurity is not just about building higher walls but creating adaptive, intelligent defenses that evolve in step with threats. Several emerging trends are shaping this outlook:

Adaptive, Risk-Based Authentication

Authentication is shifting from static checks to context-aware systems that adjust in real time. Instead of asking every user for the same login steps, adaptive authentication tailors the friction based on device, location and behavioral patterns, challenging only those deemed high risk. Industry research predicts significant growth in the adaptive authentication market, driven by rising demand for dynamic security models (FMI, 2024).

Explainable and Trustworthy AI

AI-driven fraud detection is powerful, but institutions increasingly demand transparency. The next wave of fraud systems will provide explainability, as in clear reasons why a transaction is flagged, alongside continuous learning. Studies highlight the growing need for interpretable and auditable models in finance to maintain trust and compliance (ResearchGate, 2025).

While AI-based correlations can offer early indicators and pattern detection, true reasoning and natural-language explainability are still evolving. Emerging approaches, such as using language models to generate human-readable justifications, show promise, but building fully trustworthy, auditable and reasoning-capable AI systems remain an active area of research (Naser, 2025).

AI Hallucinations and Mitigations

As AI becomes more integrated into security systems, from fraud detection to automated threat response, it introduces new classes of risks. One such risk is AI hallucination: when a model generates outputs or conclusions that are incorrect, misleading or entirely fabricated, yet appear confidently stated. In security contexts, this can result in false alarms, missed threats or even inaccurate audit trails (Naser, 2025).

To mitigate these risks, institutions should:

• Implement human-in-the-loop mechanisms for high-stakes decisions
• Use explainable AI (XAI) techniques to validate model outputs
• Set confidence thresholds and fallback rules to avoid acting on low-confidence inferences
• Continuously retrain models on verified data to reduce drift and degradation
• Conduct adversarial testing to understand how models behave under edge cases and manipulated inputs
• Log and audit AI decisions, especially when tied to security enforcement actions

AI offers speed and scale but without robust oversight, its failures can introduce new vulnerabilities. Ensuring AI reliability, auditability and accountability must now be seen as core components of the security architecture (Naser, 2025).

AI-Driven Attacks and Synthetic Identities

As defenders use AI, attackers are doing the same by leveraging deepfakes, adversarial inputs and synthetic identities to bypass defenses. Institutions must prepare for this “AI vs. AI” battle by stress-testing fraud models and deploying intentionally deceptive robust algorithms (Preprints.org, 2025).

Zero Trust Architectures

The old assumption of “trusted zones” is fading. Future payment platforms will rely on zero trust principles, where every component, API and user action must be continuously verified. This approach, combined with micro-segmentation and secure enclaves, creates a system that is resilient even if one layer is breached (ResearchGate, 2025).

Preparing for the Post-Quantum Era

Quantum computing poses a looming threat to traditional cryptography. Forward-looking institutions are already experimenting with post-quantum encryption to ensure long-term data security. Experts suggest hybrid cryptography, both classical and post-quantum, will become standard in the next decade (ResearchGate, 2025).

Conclusion

In 2025 and beyond, the financial world will depend on layered, intelligent security. AI is not a replacement for traditional safeguards; it is an essential partner to them. Together with multi-factor authentication and encryption, AI enables institutions to detect fraud in real time, prevent breaches before they occur and deliver secure digital experiences at scale.

Now is the time to strengthen your payment security strategy. Because in a digital world, trust is your most valuable currency.

References

• Europol. (2024). Internet Organised Crime Threat Assessment (IOCTA 2024). Retrieved from: https://www.europol.europa.eu/publication-events/main-reports/internet-organised-crime-threat-assessment-iocta-2024
• Fraud.com. (2023). The role of predictive analytics in fraud prevention. Retrieved from: https://www.fraud.com/post/predictive-analytics-in-fraud-prevention
• FMI. (2024). Adaptive authentication market report. Retrieved from Future Market Insights https://www.futuremarketinsights.com/reports/adaptive-authentication-market
• Kaspersky. (2024). What is multi-factor authentication (MFA)? Retrieved from: https://www.kaspersky.com/resource-center/definitions/multi-factor-authentication
• Naser, Fadi. (2025). Expert insights on AI solution integration. Personal communication, October 14, 2025.
• Preprints.org. (2025). AI-Powered Fraud Detection in Digital Payment Systems. Retrieved from: https://www.preprints.org/manuscript/202502.0278/v1
• ResearchGate. (2025). Adaptive Architecture for Secure Digital Payments. Retrieved from: https://www.researchgate.net/publication/391601180_Adaptive_Architecture_for_Secure_Digital_Payments