What is Data Poisoning? AI Impact, Examples and Best Defenses

Data poisoning is a cyberattack where attackers manipulate or corrupt training — often involving artificial intelligence (AI) systems — to undermine model performance and security. Recent research shows that poisoning as little as 7-8% of training data can cause significant failures.  

As AI becomes integral to daily operations, data poisoning is emerging as a critical risk. For financial institutions that rely on AI for credit decisions, fraud detection, and compliance monitoring, even small manipulations can distort outcomes, expose sensitive data, and spark regulatory or reputational fallout. To stay ahead of evolving threats, organizations must strengthen defenses, safeguard data, and continuously refine their cybersecurity practices. 

How does data poisoning work?

Unlike traditional cyber threats that exploit network or software vulnerabilities, data poisoning attacks the very foundation of an organization — its data — making it particularly insidious for several reasons: 

• It’s often invisible. Data poisoning can go undetected because it takes the form of “clean” data. The formatting may be correct and the labeling the same, but it’s been infiltrated.  
  
  Example: A fraud detection model is trained with seemingly valid transaction records, but attackers subtly insert mislabeled fraudulent payments as “legitimate.” The system then learns to overlook similar fraud patterns in real time. 
  
  
• It compounds over time. When data poisoning goes undetected, the poisoned data will disperse through the model as it’s continually updated, making it increasingly challenging to address and eliminate the bad data that’s fully woven into the model.  
  
  Example: An AI model used for loan underwriting incorporates new data every quarter. If poisoned data is present, each retraining cycle amplifies the manipulation, leading to growing bias or higher default risk across the loan portfolio.
  
  
• It can impact the entire organization. Like third-party risks, data poisoning can stealthily snowball into other risk areas, such as operations, compliance, and customer relations.  

Example: A corrupted monitoring model misses red flags in suspicious transactions. This creates anti-money laundering/countering the financing of terrorism (AML/CFT) issues.  

Organizations must implement a strong AI auditing and governance framework to identify control gaps. If they don’t, they’re more susceptible to data poisoning.  

Related: Don’t Fear Artificial Intelligence: A Primer for AI in Risk & Compliance Management 

Targeted vs. non-targeted attacks (Data poisoning examples)

Data poisoning attacks can be targeted or non-targeted. The difference between a targeted and non-targeted attack is the attacker’s goal. A targeted attack is designed to impact a specific function. For example, a backdoor attack might involve an attacker implanting a hidden trigger in training data to produce an incorrect output. Non-targeted attacks have a broader impact (Example: Data injections that steadily degrade the system’s performance over time).    

Related: Ransomware Risk Management: How to Defend Your FI Against Cyber Attacks 

How does data poisoning impact financial institutions? 

As more FIs integrate generative AI (GenAI) and machine learning (ML) models into their services and products, they also become more vulnerable to cyberattacks, including data poisoning. Because data poisoning corrupts the training or input data that financial institutions depend on, it can cause AI and ML models to make flawed decisions. That can mean inaccurate credit scoring, ineffective fraud detection, and potential fair lending violations. The result is cascading risk that spreads across internal teams, vendor relationships, and customer trust. 

Data poisoning in fair lending (examples)

Data poisoning can be especially destructive to lenders as they increasingly rely on AI systems to streamline loan underwriting, credit decisions, and other tasks. While federal enforcement actions for fair lending violations may have slowed in 2025, many states — including Massachusetts, which recently announced a $2.5 million settlement with a lender for fair lending and AI-driven underwriting violations — are ramping up regulation and paying special attention to how lenders are staying compliant in an increasingly AI-driven environment. 

Here are some examples of how data poisoning and its impact on fair lending: 

• Operational risk: Data poisoning undermines lending systems by distorting risk assessments and decision-making, leading to errors that directly harm borrowers. For instance, a qualified applicant may be denied a loan simply because the model misjudged their risk.
• Financial risk: A direct impact of operational risk, the financial consequences of investment decisions or inaccurate risk assessments can wreak havoc on an FI’s bottom line. For example, approving high-risk borrowers can increase default rates, while denying qualified applicants may lead to lawsuits, legal fees, and costly settlements.
• Compliance risk: Federal and state regulators take fair lending seriously, requiring lenders to test and monitor AI models as part of sound model risk management. If data poisoning goes unchecked, it can result in violations of the ECOA, FHA, and UDAAP, among other fair lending regulations.
• Reputational loss: Together, these risks can erode an FI’s credibility, driving away customers, making it harder to win new business, and undermining investor confidence, all of which add up to significant reputational harm. 

Related: 7 Fair Lending Risks You Need to Know 

Tips to defend your FI against data poisoning

While data poisoning and other cyber risks aren’t entirely unavoidable, you can ensure your FI takes proactive steps to identify, mitigate, and monitor risks:  

• Follow best practices. The Fintech Open Source Foundation’s (FINOS) AI Readiness Governance Framework offers an open-source toolkit to help FIs adopt and oversee generative AI responsibly. Designed for both technical and risk teams, it covers development, procurement, and operations, serving as a practical resource for implementing well-governed AI. 
• Make an AI inventory. Do you know how your FI and vendors are using AI? An up-to-date AI inventory helps identify where AI is in play and manage related risks, including data poisoning. 
• Refresh risk assessments. Evaluate how vendors train models, test for bias, and detect data poisoning or other adversarial attacks, and reflect these findings in your risk assessments. 
• Keep humans in the loop. While AI can streamline credit and lending decisions, human oversight is essential. Implement escalation processes to address errors or suspicious outputs. 
• Train employees. Staff can’t identify or manage AI-related risks without proper training. Ensure teams understand their responsibilities and the broader implications of data poisoning, enabling them to contribute to maintaining a robust risk and compliance culture.  
• Analyze the data. Fair lending analysis is complex, and anomalies can slip by without the right tools. Tools like regression analysis can help detect issues, explain disparities, and strengthen oversight.  

Data poisoning takeaways

Data poisoning isn’t just a technical glitch — it’s a strategic risk that cuts across operations, compliance, finance, and reputation.  

As financial institutions adopt AI at scale, the integrity of their data will determine the reliability of their decisions and the trust of their customers. The institutions that will thrive are those that treat data governance and AI oversight as core parts of their risk management strategy, investing in monitoring, training, and layered defenses — and monitoring vendor use of AI. By doing so, FIs can harness the power of AI with confidence while staying resilient against emerging threats. 

Want to learn more about AI risks and how to implement AI into the risk management lifecycle? Learn more in our free guide.