Financial data security is the foundation of trust and resilience in the Banking, Financial Services, and Insurance (BFSI) sector.
Data is no longer just an asset, it represents the bloodstream that drives the industry.
Every day, it surges through the digital arteries, enabling the enterprise to flag fraud, verify identities, approve transactions, manage loan sanctions, or predict the next market trend. With the insertion of AI models, the process has become much more intelligent, efficient, and self-capable.
However, with great intelligence and capabilities come great risks. The datasets that are used to teach large language models (LLMs) how to detect fraud could now end up getting exposed instead. Similarly, the automation that promised accelerated growth could now expand to increased vulnerabilities.
To put things into perspective, IBM’s Cost of a Data Breach Report 2025 cites the financial industry as having the second-highest average data breach cost, reaching $5.56 million in 2025.
What is Financial Data Security?
Financial data security refers to the discipline of protecting sensitive financial data against unauthorized access, exposure, loss, or misuse. The discipline or framework helps enterprises ensure that the customers’ sensitive information, like credit card numbers, transaction records, or investment details, is completely confidential, accurate, and available to use only by authorized personnel.
The types of financial data that need to be protected include:
- PII/NPI: Personally identifiable information (PII) and non-public information (NPI) refer to the type of data that can help identify an individual, such as name, contact information, etc.
- PAN/PCI: Primary account number (PAN) and payment card information (PCI) refer to data types that are protected under different types of regulations, such as Payment Card Industry Data Security Standard (PCI DSS).
- KYC/AML: Know your customer (KYC) and anti-money laundering (AML) are data types that verify legitimacy or help prevent cases of fraud or other financial crimes. These types of data include customer identity or transaction information.
A robust financial data security strategy involves a multi-tier framework, which includes sensitive data discovery and classification, access policies and controls, risk detection and remediation, and compliance reporting. By leveraging multiple data protection practices, enterprises can effectively prevent data breaches, identify suspicious access patterns early, and demonstrate compliance to garner trust amongst customers and partners.
Apart from trust and reputation, effective cybersecurity for financial institutions further ensures the safe use of data. Teams can leverage data confidently for various business purposes, such as analytics, business intelligence, product enhancements, automation, and innovation.
Financial Data Protection For AI
AI is transforming industries across the globe. In fact, it has become a core component in the financial industry as well. For instance, with AI algorithms, banking and financial institutions can assess the creditworthiness of customers in real time, detect fraud or abnormal behavior patterns, predict loan defaults or credit risk ratings, and personalize recommendations based on clients’ spending or saving behavior.
However, the proficiency of financial AI models or applications is as good as the data they use to train or fine-tune on. On the contrary, the models that unlock accelerated innovation could amplify risks. Take, for instance, the 2024 AI Benchmarking Survey found that 92% of financial firms have yet to adopt an adequate AI governance framework and oversight mechanisms, despite the enthusiasm to accelerate AI adoption.
Modern data and AI security strategies focus on where traditional frameworks struggle:
- Data and AI visibility: Getting clear visibility into sanctioned and shadow AI across the enterprise environment. Monitoring PII/NPI, PAN/PCI, KYC/AML data flows and AI interaction.
- Data exposure prevention: Discovering and classifying regulated financial data, detecting risks caused by misconfigurations, shadow data, or data oversharing, and automating remediations effectively.
- Data access governance: Enforcing least-privilege access by detecting LLMs, users, or services’ access to regulated or sensitive data, monitoring access patterns or anomalies, and implementing policy-based access controls.
- Compliance automation: Streamlining compliance operations through a built-in regulatory knowledge base, automated evidence collection, and auditor-ready compliance reporting.
- ROT data minimization: Reducing attack surface and storage cost through duplicate data identification and automated deletion, and AI efficacy improvement.
Common Threats to Financial Data
Banking, financial, and insurance services face a myriad of cybersecurity threats throughout the year. Understanding these different types of data and AI threats is important to effectively implement appropriate security measures accordingly.
a. Phishing and Social Engineering
It is a well-known fact in the cybersecurity space that humans are the weakest link because they can be easily tricked into exposing sensitive information. Phishing or social engineering is among those types of cyberattacks that manipulate the psychological behavior of humans. For instance, an attacker may pose as a CEO of the company and may prompt any employee to click on any malicious link or share the company’s confidential information. In fact, the cost of data breaches caused by phishing attacks clocks at $4.80 million per breach.
b. Insider Threats
Insider threats are one of the top, most common initial attack vectors, standing at an average $4.92 million per breach. These types of threats are equally challenging as well as detrimental to financial institutions, regardless of whether they are intentional or unintentional. Intentional threats may include data theft or sabotage, while unintentional threats may include accidental data leak or exposure, etc.
c. Ransomware Attacks
Ransomware attacks are also the most common types of cyber threats that enterprises tend to face, regardless of the industry. Here, attacks infiltrate a corporate network or data resources, encrypting them and making them unable to be accessed. As a result, a heavy ransom is demanded from enterprises in exchange for access to their sensitive data. Practices like least privilege access controls, data anonymization, and periodic data backups can help financial institutions prevent the consequences of ransomware attacks.
d. Insecure Third-Party Vendors
It is imperative that data security go beyond an enterprise’s environment, expanding to third-party vendors. A 2024 study by Blackberry found that 75% of enterprises experienced supply chain attacks last year. Furthermore, 74% of the attacks were found to originate from third-party vendors in the supply chain that the breached enterprises were either unaware of or didn’t effectively monitor.
e. The OWASP Top 10 Risks for LLMs
AI or LLMs have introduced a multitude of risks and vulnerabilities that not only put enterprises at risk of serious cybersecurity incidents but also hinder safer AI adoption and innovation. The Open Worldwide Application Security Project (OWASP) has listed those risks in its 2025 list. For instance, without effective data and AI access controls in place, sensitive data could be exposed to unauthorized individuals, resulting in sensitive information disclosure. Similarly, compromised LLM models or third-party integrations could put the enterprise at risk of supply chain attacks.
Regulatory Compliance Standards and Frameworks in the Financial Sector
The financial industry deals with one of the highly regulated types of data. These data types are governed by stringent data privacy and protection laws, as well as industry-specific frameworks. Understanding these laws and frameworks is crucial to ensuring robust data security, accountability, operational integrity, and compliance assurance.
a. PCI DSS (Payment Card Industry Data Security Standard)
The Payment Card Industry Data Security Standard (PCI DSS) is a global financial-industry-specific framework that is built to govern and protect cardholder data, preventing cases of fraud. The latest version of PCI DSS, i.e., v4.0, categorizes organizations into four levels that are based on annual transaction volume, ranging from 6 million (Level 1) to 20,000 (Level 4).
Similarly, the latest framework discusses 12 key areas, which include protection of stored and transmitted data, implementing and maintaining firewalls, enforcing access policies and controls, testing systems, and monitoring activities, to name a few.
Learn more about: What is PCI DSS and Its 12 Requirements to Comply
b. SOX (Sarbanes-Oxley Act)
The Sarbanes-Oxley Act, also known as the Public Company Accounting Reform and Investor Protection Act of 2002 (SOX), was passed by the United States Congress in response to unprecedented financial scandals in the early 2000s. SOX requires the top management of public finance companies to implement robust internal controls and guarantee the accuracy and transparency of public financial disclosures.
The key provisions of the Act include Section 302, that businesses must verify the accuracy of financial statements and disclose any deficiencies; Section 401 demands the assessment and reporting of internal controls’ efficiency, or Section 802 imposes penalties on violators who are found to be altering or destroying financial documents.
Download Whitepaper: An Overview of the Sarbanes-Oxley Act
c. GDPR (for EU businesses)
The General Data Protection Regulation (GDPR) is the EU’s primary data protection law that has also influenced and inspired many other regulations across the globe. For the banking, financial, and insurance industry as well, the GDPR carries important provisions that are critical to protecting customers’ sensitive financial information. For instance, for data integrity and accuracy, Article 5 of GDPR carries somewhat similar provisions as sections 302 and 304 of the SOX Act.
The GDPR further requires organizations to ensure data minimization by collecting only necessary data. It further imposes purpose limitations by requiring organizations to use customers’ financial data for only the purpose for which the data is collected, such as fraud detection or credit score assessment.
d. NIST Cybersecurity Framework
The NIST cybersecurity framework (CSF) is considered a benchmark of robust cybersecurity governance. Developed by the U.S. National Institute of Standards and Technology (NIST), the framework offers a risk-based and adaptive approach to cybersecurity. NIST CMF is built around five core functions that work in tandem, offering optimal risk management.
Let’s take a quick look at the core functions:
- Identify: Discover and understand the critical risks involving systems and data.
- Protect: Establish guardrails to protect sensitive assets and data, preventing breaches.
- Detect: Continuously monitor systems to detect critical cybersecurity events, such as breaches.
- Respond: Create and implement an action plan to detect, analyze, and mitigate cybersecurity incidents.
- Recover: Restore services and bring operations back to normal while also improving the resilience of the overall framework.
e. GLBA
The Gramm-Leach-Bliley Act (GLBA), formerly known as the Financial Services Modernization Act of 1999, is a US federal law. The regulation aims to offer enhanced privacy protection to customers in the US, requiring financial institutions, banks, investment companies, and insurance firms to protect customers’ personal financial information.
The primary objectives of the act are to first ensure the privacy of customers and promote transparency with regard to how their data is collected, used, shared, and protected. The Act has three core components.
The Financial Privacy Rule requires regulated entities to offer privacy notices, clearly informing customers of the collection, use, disclosure, and protection of their data. The Safeguards Rule requires entities to establish and implement a comprehensive cybersecurity program to safeguard customers’ data, preventing data theft, misuse, or unauthorized access. The Pretexting Provision prohibits financial institutions from collecting customer information through deceptive practices.
Learn more about: The Gramm-Leach-Bliley Act (GLBA)
Best Practices for Financial Data Protection
Financial data security is critical to any organization’s customers’ privacy and trust. Implementing best practices can help organizations ensure that their customers’ data is protected against exposure, unauthorized access, and misuse.
a. Accelerate Safe AI Adoption
As AI plays an integral role in almost every industry, including finance, it is imperative that an enterprise's cybersecurity strategy unify data and AI security. Start with gaining visibility of sanctioned and unsanctioned (shadow) AI systems across the enterprise. Discover and classify datasets that are used to train or fine-tune AI models, such as PII, NPI, AML, KYC, etc. A comprehensive data map can give detailed insights into models and the data that flows through them.
It is also critical to understand and track the interaction of data with different systems, resources, networks, and LLMs. This allows organizations to implement appropriate security measures to ensure safe use of data, such as data masking, access policies and controls, and LLM firewalls, to name a few.
b. Find, Classify, and Prevent Financial Data Exposure
Data security starts with locating and understanding the data an enterprise has across its environment. So, automate the discovery and classification of important, regulated financial data across public cloud, hybrid cloud, and SaaS environments. This data may include PII, NPI, KYC, proprietary data, and intellectual property data, to name a few.
Once security teams have a bird’s-eye view of their organization’s data landscape, it allows them to efficiently detect and probe into risky exposures. For instance, a misconfigured AWS bucket with sensitive data could result in a serious data breach. Similarly, shadow data, orphaned datasets, and data oversharing could all lead to sensitive data exposure
A comprehensive visibility and understanding of data can further allow teams to create effective policy-driven fixes for automated remediation.
c. Enforce Least-Privilege Access
Studies reveal that multicloud environments have, on average, 40,000 different permissions to manage, while 50% of them are high risk. Excessive privilege is one of the top factors that may cost businesses data theft and exploitation. Therefore, it is critical that organizations strive for a least privilege access model.
Organizations must start by getting visibility of access permissions across their data and AI landscape, which includes users, systems, workloads, and AI Agents. Map and correlate financial data, users, identities, and access entitlements to get a better understanding of access risks. For instance, by knowing which users and AI Agents are accessing sensitive data and whether there are abnormalities in access patterns, teams can easily identify insider risks, unauthorized access, over-provisioned identities, and orphaned permissions
By leveraging these access activity insights and enforcing policy-based controls, organizations can right-size access permissions without blocking data access altogether.
d. Automate Compliance Testing and Reporting
Leverage regulatory intelligence, control testing controls, and automated reporting to demonstrate continuous compliance. Regulatory intelligence may mean having a comprehensive built-in library that is mapped to various data protection regulations and industry-specific frameworks like GLBA, SOX Act, PCI DSS, and DORA etc.
Automated compliance testing and on-demand, auditor-ready reports can greatly streamline audits for regulators, executives, and board members. This will help reduce manual effort and compliance fatigue. Furthermore, built-in remediations proactively enforce policies across data, models, and AI systems, minimizing risk and proving compliance.
e. Reduce Data Sprawl and Safe Cost
Shrink PCI and DORA scope while reducing attack surfaces and storage costs through intelligent data hygiene. Automatically identify and remediate duplicates, stale files, and shadow copies of sensitive financial data across multicloud, data lakes, SaaS, and collaboration platforms.
Streamline operations by archiving or deleting redundant PII, PAN, KYC, AML, trading, and payment data, ensuring only relevant information remains. Cleansing outdated datasets not only strengthens data security posture but also enhances AI accuracy and model performance.
Financial Data Security Has Always Been Crucial. Now It’s Paramount in the AI Era
Financial data security has evolved from cybersecurity and regulatory compliance into a core pillar of customer trust. Ever since the rise of generative AIs and LLMs, cybersecurity threats have also evolved, requiring proactive vigilance and data protection. As a result, financial institutions must now balance innovation with the safe use of data, ensuring no open pathways that could result in exploitation.
Securiti can empower financial institutions, banks, and insurance firms to harmonize data and AI privacy, security, and compliance, enabling them to protect the integrity of financial systems efficiently and accelerate innovation safely.
