The Future of Financial Cybersecurity: Protecting Consumer Data in the Age of AI

We’ll take a look at the state of cybersecurity for FIs leveraging AI, diving into the challenges they face, best practices and key features to look for in an AI provider.

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Financial institutions have officially entered the age of artificial intelligence, with nearly 99% of financial services leaders deploying AI in some capacity, according to EY. However, in this rapidly evolving landscape, robust data protection is paramount. From emerging cyber threats to changing regulations, FIs must use caution when adopting AI, ensuring that both they and their solution provider have the necessary defenses to safeguard their sensitive information.

In this blog, we’ll take a closer look at the state of cybersecurity for FIs leveraging AI, diving into the challenges they face, best practices and key features to look for in an AI provider.

Common Threats Facing FIs

Let’s start by examining some of the key cybersecurity threats facing FIs:

  • Phishing and Spearphishing Attempts: Phishing and spearphishing are some of the most common forms of cyberattacks, designed to deceive employees or customers into disclosing confidential financial information or credentials.
  • Malware: Viruses and Ransomware can be used to steal sensitive information, extort money and disrupt the operations of the FI.
  • Infrastructure Vulnerabilities: Aging or poorly maintained IT infrastructure presents significant vulnerabilities for FIs, leaving them open to attacks that can compromise consumer data.
  • Third-Party AI Risks: As FIs adopt new AI solutions for improving customer service and operational efficiency, they must also ensure their AI provider has the necessary policies, protocols and certifications in place to protect their data.

In addition to all of these challenges, FIs must also worry about the rapid escalation of cybersecurity attacks. According to the Identity Theft Resource Center, 744 financial services companies were compromised in 2023, resulting in around 61 million victims — a staggering 176% increase in breaches on FIs from 2022. While these compromises aren’t attributable to AI tools, any new piece of connected technology can pose a security risk, potentially exposing you to unforeseen vulnerabilities.

At the same time, regulations around AI and data security are constantly evolving, especially in a high-stakes industry like financial services. 

Staying Compliant in Today’s Regulatory Landscape

Navigating the complex web of state, federal and international regulations can pose a significant challenge for FIs, especially when the legal landscape is continuously shifting. For instance, the Biden-Harris Administration recently announced an executive order emphasizing the safe, secure and trustworthy development of AI, as well as the need for FIs to update their cybersecurity practices.

As regulations evolve, FIs must remain agile, reviewing and refreshing policies and practices to not only stay compliant but also avoid the hefty fines, reputational damage and even legal penalties that come with a data breach.

To help, here are a few of the key bills and frameworks currently guiding financial services operations when it comes to data privacy and AI:

Gramm-Leach Bliley Act

The Gramm-Leach Bliley Act (GLBA) mandates that FIs ensure the confidentiality and security of consumer financial information. It requires institutions to be transparent about their information-sharing practices and to protect sensitive data from foreseeable threats in cybersecurity and data integrity. For FIs leveraging AI, this means implementing robust encryption, access controls and regular audits to comply with GLBA’s Financial Privacy and Safeguards rules.

California Consumer Privacy Act

California’s Consumer Privacy Act is a state statute that grants California residents certain privacy rights and greater control over their personal information. This act represents a significant milestone in the U.S.’s approach to consumer data protection, underscoring the importance of transparent data practices and consumer-centric security measures. FIs operating in California or dealing with state residents’ data must comply with CCPA’s stringent requirements, which include providing clear disclosures about data collection practices and allowing consumers to opt out of their data being sold.

For FIs operating outside of California, check out your state privacy laws here.

The Foundations of FI Data Security

As FIs increasingly adopt and integrate AI into their operations, these regulations are bound to evolve and adapt to the changing technology. Having a strong set of foundational security principles will be essential to protect sensitive customer data against cyber threats.

In the emerging field of AI, there are two cornerstones of FI data security: the well-established CIA Triad and the National Institute of Standards and Technology’s (NIST’s) AI Risk Management Framework. Safety, security, resilience, explainability, interpretability, privacy enhancement, and fairness form the bedrock of ensuring an AI system is "Valid and Reliable." These, coupled with accountability and transparency, are indispensable criteria for deeming an AI system "trustworthy." Together, these principles offer a comprehensive approach to safeguarding financial data, ensuring that FIs can leverage AI’s benefits without compromising on security.

1. The CIA Triad

The CIA Triad model outlines three core tenants of information security: Confidentiality, Integrity and Availability. Confidentiality ensures that sensitive information can only be accessed by authorized individuals. Integrity protects data from being improperly modified or deleted, maintaining accuracy and trustworthiness. Last, but not least, availability guarantees that data and systems are accessible to authorized users when needed, preventing downtime or data loss that could impact operations.

To align with the CIA Triad, organizations must embed these principles into AI systems and processes, ensuring that AI-powered services enhance rather than compromise data security. This is crucial for building trust and confidence among consumers and stakeholders in the high-stakes financial services sector.

2. NIST’s Artificial Intelligence Risk Management Framework (AI RMF 1.0)

NIST has also established critical guidelines for the age of AI, emphasizing the importance of creating ethical AI systems that are safe and reliable. Designed for adaptable use, this new framework empowers solution providers with strategies to enhance their AI system trustworthiness, promoting responsible design, development, deployment and usage. For FIs, choosing a partner that follows AI RMF 1.0 will be instrumental in managing and mitigating the risks associated with AI, both now and in the future.

What To Look For in a Third-Party Provider

When looking for an AI solutions provider, financial institutions should prioritize partners who are native to the AI space and demonstrate a strong commitment to data security. But what exactly does this look like?

1. They Don’t Retain Sensitive Information

An AI solution provider for the financial services industry should never retain the sensitive information they handle beyond what is necessary. This lowers the risk of data breaches by reducing the volume of data that could be exposed.

FIs should seek AI solution providers that employ robust data minimization strategies, demonstrating a strong commitment to protecting consumer privacy and adhering to data protection regulations. This will safeguard sensitive information and build trust with consumers.

2. They Leverage Data Loss Prevention Techniques

Data loss prevention (DLP) is essential for AI solution providers. DLP services can redact sensitive information like Social Security Numbers to prevent it from being stored and ensure the redaction cannot be undone. A competent AI provider employs advanced DLP strategies and techniques to prevent unauthorized access and data exfiltration. This includes monitoring stored logs as additional validation that the redaction process didn’t miss anything.

These techniques ensure that FIs can protect their consumer data across all stages of processing and storage, minimizing the risk of accidental or malicious data breaches while ensuring compliance with stringent regulatory standards.

3. They Undergo Regular Third-Party Auditing

Auditing services as a crucial mechanism for continuous improvement, pushing AI providers to consistently enhance their security measures. It’s best to choose a provider that willingly subjects itself to regular third-party audits. These are inspections conducted by independent entities to rigorously assess the provider’s compliance with industry standards and regulations, including: 

Certifications like these are not mere accolades but a testament to the provider’s dedication to maintaining high security and privacy standards. This level of transparency allows FIs to rest easy knowing they’re partnering with companies that uphold the integrity and security of consumer data.

4. They Have a Mature Security Program

A mature security program is foundational to any AI solutions provider, especially those serving the financial services sector. These programs encompass not just the technical aspects of security, such as disaster recovery plans and incident response strategies, but also the cultural commitment to security best practices.

AI providers should demonstrate robust programs and practices, including: 

  • Implementing strict access controls based on the principle of least privilege.
  • Encrypting data in transit and at rest.
  • Ensuring proper network segmentation.
  • Maintaining 24/7/365 security monitoring and alerting. 

All this aids in the early detection and prevention of threats. By partnering with a provider who has a well-established security program, FIs can gain an added layer of protection rather than a new vulnerability.

As institutions navigate through the complexities of cybersecurity in the AI era, they can trust Posh to keep their best interests in mind and protect their sensitive data. Our AI solutions are designed with security at their core, leveraging data minimization strategies, advanced DLP techniques and regular third-party audits to meet and exceed industry standards.

Discover how Posh is redefining data security and privacy in the financial sector.

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