The risks of using LLMs in business intelligence
Business Intelligence
Jun 13, 2025
Large Language Models in business intelligence offer great potential but pose significant risks, including data privacy issues and accuracy concerns.
Large Language Models (LLMs) are transforming business intelligence (BI), but they come with serious risks. Here’s a quick summary of what you need to know:
Data Privacy Issues: Sensitive data processed through external platforms can lead to breaches. For example, Samsung employees accidentally leaked confidential data using an LLM.
Accuracy Problems: LLMs often generate false or misleading information (hallucinations), which can result in costly business mistakes.
Security Vulnerabilities: Models are vulnerable to attacks like prompt injections, training data poisoning, and model theft.
Ethical and Compliance Challenges: Bias in LLMs can lead to discriminatory outcomes, legal risks, and reputational harm.
Key Risks and Solutions
Risk | Impact | Mitigation Strategy |
|---|---|---|
Data Privacy & Security | Breaches of sensitive business data | Encrypt data, use Role-Based Access Control (RBAC), and limit data collection |
Accuracy Issues | Poor decisions based on false insights | Cross-verify outputs with trusted sources and add human oversight |
Prompt Injection Attacks | Malicious manipulation of models | Validate inputs, sanitize data, and test for vulnerabilities |
Ethical and Compliance Risks | Reputational damage and legal penalties | Set ethical guidelines, anonymize data, and ensure compliance with regulations |
Takeaway: To safely use LLMs in BI, businesses must implement strong security measures, continuous monitoring, and ethical guidelines. These steps reduce risks and ensure reliable, secure insights from AI systems.
Avoiding the Pitfalls When Using LLMs for BI
Main Risks of Using LLMs in Business Intelligence
Incorporating large language models (LLMs) into business intelligence processes can introduce serious risks, potentially affecting data security, decision-making, and compliance. Businesses must carefully assess these risks to protect their operations and maintain trust.
Data Privacy and Security Issues
Protecting sensitive information is critical when using LLMs in business intelligence. These systems often process data on external cloud platforms, which can expose sensitive business information to unauthorized access or loss of control [1]. A notable example occurred in April 2023, when Samsung Electronics employees unintentionally leaked confidential corporate data by entering it into a public LLM. Additionally, LLMs can inadvertently expose personal or confidential details in their outputs [2]. Research from the University of North Carolina highlights that even after applying data removal methods, sensitive information can still surface in LLM outputs [4].
Advanced threats, like prompt injection attacks, training data poisoning, and model theft, further compromise system security. Larger models, in particular, are more vulnerable to even small-scale contamination, intensifying risks to accuracy and reliability [4].
Accuracy Problems and False Information
LLMs are also prone to generating incorrect or misleading outputs, commonly referred to as hallucinations. These errors can lead to costly mistakes. For instance, Gartner reports that companies lose millions each year due to inefficiencies caused by poor data quality. Alarmingly, only about 10% of businesses experimenting with AI have reached a mature level of implementation [5]. Real-world examples include Air Canada’s February 2024 incident, where incorrect fare details caused financial and operational damages, and a 2023 case where attorneys cited fabricated legal cases generated by ChatGPT [6][7]. Other notable failures include McDonald’s halting its AI-driven ordering system and Zillow suffering significant financial losses due to miscalculations [7].
"When an AI hallucination produces a plausible but false statement, the reputation of the organization utilizing the LLM can suffer, potentially leading to market-share losses." - Huzaifa Sidhpurwala, Senior Principal Product Security Engineer, Red Hat [6]
Ethics and Compliance Challenges
Algorithmic bias is a major ethical concern when using LLMs in business intelligence. Biases embedded in these models can perpetuate stereotypes or lead to discriminatory outcomes, which may expose companies to legal risks [8].
"Even when technology automates the discrimination, the employer is still responsible." - Charlotte Burrows, Former EEOC Chair [7]
For businesses, these ethical missteps can result in reputational harm and legal consequences. The opaque nature of LLMs - often referred to as a "black box" - makes it difficult to ensure accountability and transparency, further heightening risks related to intellectual property and bias [8]. Companies must also comply with stringent data privacy regulations, such as the California Consumer Privacy Act (CCPA), which requires robust data protection measures [9]. Moreover, LLMs can produce deepfake content, misleading information, or spam, all of which can tarnish a company’s reputation and invite regulatory scrutiny.
System Security Weaknesses
LLM systems face several security vulnerabilities in business intelligence settings. One significant risk is prompt injection attacks, where malicious actors manipulate the model through carefully crafted inputs to perform unintended actions or disclose sensitive data [3]. Model theft, where attackers extract proprietary training data or parameters, poses a financial threat by undermining a company’s investments [4]. Additionally, insecure output handling and supply chain weaknesses leave systems open to threats like cross-site scripting (XSS) attacks and privilege escalation [4].
Weak access control mechanisms, such as insufficient authentication protocols, further exacerbate security risks. Without robust measures like role-based access controls and multi-factor authentication, unauthorized users could gain access to models or configurations [3]. To mitigate these risks, businesses must implement comprehensive logging systems to monitor inputs and outputs, detect suspicious activity, and ensure the integrity of LLM-generated content [4].
How These Risks Affect Business Intelligence Operations
These risks disrupt workflows, compromise decision-making, and lead to steep financial losses. When vulnerabilities surface, they can cripple data operations and cause both monetary and reputational harm. Let’s take a closer look at how these challenges impact business intelligence (BI) operations.
Data Breaches and Privacy Violations
When sensitive information is exposed through large language models (LLMs), the damage to BI operations can be catastrophic. Data breaches cost companies an average of $4.88 million, with downtime expenses reaching up to $88,000 per hour [10]. Alarmingly, 83% of organizations reported experiencing more than one data breach in 2022 [12], underscoring how frequent and disruptive these incidents have become.
The fallout from such breaches doesn’t stop at the initial event. BI processes often need to be paused to contain the breach, assess the damage, and implement emergency measures. For example, in March 2023, OpenAI’s ChatGPT exposed payment and other sensitive details of 1.2% of its ChatGPT Plus subscribers due to a bug in an open-source library [12]. If BI teams rely on similar platforms, they risk exposing customer data, financial records, and proprietary analytics - key assets for their operations.
Even simple employee mistakes can jeopardize BI data, requiring investigations, forensic analysis, and process overhauls. These disruptions can grind workflows to a halt for weeks. Additionally, when sensitive business information is uploaded to LLM platforms hosted on external cloud infrastructure, BI teams lose control over where the data is stored, how long it’s retained, and whether it complies with internal data policies.
Wrong Reports and Bad Business Decisions
Inaccurate AI outputs can lead to flawed insights and poor decisions, creating a domino effect throughout an organization. For instance, poor decision-making can cost businesses at least 3% of their profits [13]. For a $5 billion company, this translates to a staggering $150 million in annual losses.
Even minor inaccuracies can snowball. A faulty revenue forecast from an LLM could result in misallocated budgets, misguided hiring plans, or strategic missteps. These errors often go unnoticed until considerable damage has already been done.
Another challenge is the overwhelming volume of data generated by LLMs. While more information might seem beneficial, it can delay critical decisions. Research by Oracle and Seth Stephens-Davidowitz found that 85% of business leaders have experienced decision stress, with three-quarters reporting a tenfold increase in the number of daily decisions they must make over the past three years [13]. Introducing unreliable data into this already high-pressure environment only adds to the chaos.
Take Air Canada’s chatbot incident as an example. The chatbot incorrectly informed a customer that tickets could be refunded after travel completion, even though refunds were only allowed before travel. This error led to a lawsuit, and the customer won after the company refused compensation [14][15]. Such incidents highlight how confidently delivered but incorrect information can wreak havoc.
Business Disruptions from Security Failures
Security failures can bring BI operations to a standstill while driving up costs. With AI spending projected to hit $13.8 billion in 2024 - a sixfold increase from 2023 [15] - these systems are becoming prime targets for cyberattacks.
One major threat is prompt injection attacks, which can completely disrupt BI workflows. In one case, a company (referred to as “DeepQuery” for anonymity) experienced a devastating attack on its LLM-powered customer service system. This breach exposed sensitive customer data and proprietary information, resulting in millions of dollars in damages and severe reputational harm [16].
Another growing concern is model theft. When attackers extract proprietary training data or parameters, they effectively steal the intellectual property that sets companies apart. Organizations are then forced to rebuild their models, incurring significant costs and delays.
Denial-of-service (DoS) attacks pose yet another challenge. Excessive resource usage caused by these attacks can degrade system performance, slowing or halting legitimate BI queries. This creates bottlenecks that ripple across the organization, hampering access to critical insights.
The rapid adoption of LLMs magnifies these risks. By 2026, over 80% of enterprises are expected to use or experiment with LLMs, up from less than 5% in 2022 [16]. With 72% of U.S. decision-makers expanding their use of generative AI tools [15], the attack surface continues to grow.
As Rik Turner, Senior Principal Analyst for Cybersecurity at Omdia, cautions:
"If it comes back talking rubbish and the analyst can easily identify it as such, he or she can slap it down and help train the algorithm further. But what if the hallucination is highly plausible and looks like the real thing?" [15]
Rob T. Lee, Chief of Research and Head of Faculty at the SANS Institute, adds:
"Organizations considering deploying generative AI technologies need to consider the risks associated with it. The OWASP top ten does a decent job at walking through the current possibilities where LLMs could be vulnerable or exploited." [15]
These examples highlight the vulnerabilities that directly impact BI performance and strategic decision-making, making it clear that organizations must tread carefully as they integrate LLMs into their operations.
How to Reduce LLM Risks in Business Intelligence
Once key risks are identified, these strategies can help secure business intelligence (BI) operations effectively.
Protecting Data and Privacy
Start by evaluating where your data might be vulnerable. A 2023 study from Cyberhaven found that companies leaked sensitive data to ChatGPT hundreds of times weekly [17].
To safeguard your data, implement Role-Based Access Control (RBAC) and Multi-Factor Authentication (MFA) to limit access to sensitive BI information. Secure API endpoints with authentication tokens and regularly review access permissions [17].
Keep data collection and retention to a minimum. This reduces the potential damage in the event of a breach [17].
Encryption is another key defense. Encrypt data both at rest and in transit - not just for primary datasets but also for training and testing data [17]. This ensures that even if attackers gain access, the data remains unusable.
Validate and sanitize data to protect against malicious code injection. Before processing data through LLM systems, check for proper type, range, format, and consistency [17].
Finally, adopt continuous monitoring. Use tools to track access controls, system logs, and data usage patterns in real time. This allows you to detect suspicious activity early and address it before it escalates [11].
These measures provide a strong foundation for testing and monitoring your LLM systems.
Testing and Monitoring Models
Building on secure data practices, ongoing testing and monitoring are critical to managing LLMs. Since these models are non-deterministic and lack a clear ground truth, specialized monitoring is necessary to track both technical performance and output quality [18].
Start by defining clear monitoring objectives. Identify critical business functions and translate them into measurable performance indicators for your LLM [18].
Set up real-time monitoring to capture the entire interaction lifecycle. This includes input prompts, raw outputs, processed responses, latency metrics, and metadata. Such visibility helps you evaluate both technical and qualitative outcomes [18].
Incorporate guardrail metrics and centralized dashboards for quick issue detection and resolution [18][19].
Establish feedback loops that combine automated monitoring with human evaluation. Collect feedback through explicit channels, like user ratings, as well as implicit signals, such as user behavior patterns, to continuously improve the model's performance [18].
Conduct regular adversarial testing to identify vulnerabilities. Techniques like token manipulation, gradient-based attacks, and jailbreak prompting tests can help strengthen your defenses. Red teaming is another valuable approach to uncover potential weaknesses [19].
"LLM monitoring has become a critical concern for AI teams as they push language models into production environments." - Conor Bronsdon, Head of Developer Awareness, Galileo [18]
Ethics Rules and Legal Compliance
Beyond technical safeguards, ethical practices and legal compliance are essential in managing LLM deployments responsibly.
Setting ethical guidelines isn’t just about avoiding penalties - it’s about earning trust from customers and stakeholders. Recent cases demonstrate how organizations are tackling these challenges.
Develop clear ethical guidelines and conduct regular audits to ensure LLM usage aligns with regulations. For instance, Google Cloud partnered with Mayo Clinic to implement HIPAA-compliant generative AI software, enabling secure access to patient records, clinical protocols, and research papers [9][20].
Leverage LLMs to stay ahead of regulatory changes, reduce compliance risks, and improve operational efficiency. This proactive approach also strengthens stakeholder trust [9].
Use techniques like data anonymization and privacy-preserving machine learning to protect sensitive information. Tools such as data privacy vaults and Zero Trust architecture can further reduce the risk of exposure [21].
Regularly update and retrain models to comply with evolving privacy standards. Conduct thorough risk assessments to identify potential privacy concerns and ensure your AI systems meet current regulatory requirements [21].
"Compliance builds trust and safeguards your reputation. And in today's world, trust is worth its weight in gold." - Robyn Ferreira, Senior GRC Manager, Scytale [20]
Security Best Practices
With data protections and monitoring in place, strengthen your defenses with robust security practices to protect every layer of your BI operations.
Security failures can disrupt entire BI systems, so a comprehensive risk management strategy is essential. Interconnected defenses create multiple layers of protection.
Protect your APIs with proper authentication, authorization, and rate limiting, as these are common targets for attackers. Conduct regular vulnerability assessments and penetration testing to identify and address security gaps before they can be exploited.
Apply encryption, implement strong access controls, and maintain continuous monitoring across all systems.
Prepare for AI-related security events by establishing clear incident response procedures. A well-defined response plan minimizes damage and speeds up recovery when attacks occur.
Risk and Solution Comparison Table
This table connects common risks associated with Large Language Models (LLMs) to specific strategies designed to reduce those risks. By addressing vulnerabilities systematically, you can build a layered security approach that prioritizes protection at multiple levels.
Risk | Potential Impact | Mitigation Strategy |
|---|---|---|
Data Privacy and Security Issues | Exposure of sensitive business data stored on external cloud systems; risk of unauthorized access to confidential information | Use Role-Based Access Control (RBAC) and Multi-Factor Authentication (MFA); encrypt data both at rest and in transit; employ data sanitization and privacy-preserving machine learning techniques |
Accuracy Problems and False Information | Misleading outputs or incorrect results could lead to poor decisions; "hallucinations" where LLMs generate plausible but false responses | Incorporate Retrieval-Augmented Generation (RAG); cross-verify outputs with trusted sources; establish human oversight alongside automated validation processes |
Prompt Injection Attacks | Malicious input manipulation to extract sensitive data or bypass security protocols | Implement strict input validation and sanitization; separate user inputs from AI responses; perform regular testing for prompt injection vulnerabilities |
Training Data Poisoning | Degraded model performance due to malicious or corrupted training data, undermining response reliability | Utilize trusted data sources; set up data validation pipelines; audit and update training datasets regularly |
Denial of Service Attacks | Service disruptions, similar to the November 2023 ChatGPT outage, causing downtime and operational delays | Introduce rate limiting and load balancing; manage resource allocation effectively; apply timeouts and throttling to prevent overloads |
Insecure Output Handling | Harmful code or sensitive data in unfiltered outputs being passed to downstream systems | Enforce strong output filtering and sanitization; monitor and log all LLM interactions |
Supply Chain Vulnerabilities | Risks from third-party plugins or components that could compromise broader systems | Use components from reputable vendors; thoroughly test plugins; maintain a centralized inventory of ML models with automated deployment controls |
Excessive Agency and Overreliance | Overdependence on LLM outputs can lead to automated decisions based on flawed information | Restrict LLM agent permissions; require user authorization for critical actions; implement human-in-the-loop systems and monitor outputs regularly |
Model Theft and IP Exposure | Unauthorized access to proprietary models or algorithms, leading to significant financial losses | Use strong authentication; encrypt model data and code; secure physical storage environments with access controls |
Compliance and Ethical Violations | Regulatory penalties and reputational damage due to non-compliant AI use; average data breach costs are projected at $4.88 million in 2024 [24] | Develop clear ethical guidelines; conduct compliance audits regularly; adopt HIPAA-compliant or industry-specific solutions; anonymize data where applicable |
This table highlights the importance of a multi-layered defense strategy. Each mitigation approach addresses specific risks while reinforcing overall security. Many strategies overlap, providing added protection for data privacy, compliance, and intellectual property.
With 94% of executives prioritizing risk management [23], a structured approach like this is critical for maintaining a competitive edge.
"Implementing LLM security involves integrating offensive and defensive strategies during the model's development lifecycle. This includes careful management of the training data, deploying secure infrastructure, and regularly updating systems to defend against emerging threats." - Perception Point [22]
Conclusion
Integrating Large Language Models (LLMs) into business intelligence workflows brings both opportunities and challenges, requiring careful attention to risks. With 80% of organizations now using LLMs regularly [25], managing potential issues like data privacy breaches, accuracy errors, ethical concerns, and system vulnerabilities is critical. Ignoring these risks can lead to serious consequences, including legal and privacy complications, as highlighted by recent cases of fabricated citations and data exposures [25].
These challenges are not going unnoticed. A significant 71% of IT leaders have expressed concerns about these risks [25], emphasizing the need for strong access controls, human oversight, and ongoing monitoring. Together, these measures create a secure foundation for integrating AI into business intelligence, echoing the multi-layered strategies discussed earlier.
Industry leaders are already stepping up to address these challenges. For example, Querio is committed to providing secure, ethical, and reliable AI-driven solutions tailored to US-based organizations. By blending advanced AI capabilities with rigorous security protocols, robust data privacy measures, and clear ethical standards, Querio enables businesses to leverage LLMs while safeguarding data integrity and meeting regulatory requirements.
Ultimately, successful AI adoption isn’t a one-time effort - it’s a continuous process. With the right safeguards, regular monitoring, and a strong commitment to ethical practices, businesses can confidently use LLMs to unlock secure and meaningful insights from their data.
FAQs
How can businesses use LLMs in business intelligence while protecting data privacy and ensuring security?
To make the most of large language models (LLMs) in business intelligence while keeping data privacy and security intact, businesses need a thoughtful approach. One key strategy is data minimization - process only the data that's absolutely necessary. This reduces the chances of exposing sensitive information and keeps risks under control.
It's also essential to establish strong access controls. Limit who can access the system, and back this up with regular audits to spot any unauthorized access or suspicious activity.
Another area to watch out for is prompt injection attacks, where harmful inputs could manipulate the model's responses. To counter this, use prompt filtering and keep a close eye on interactions to catch any vulnerabilities early. These attacks, if left unchecked, could lead to data breaches or even regulatory trouble.
By putting these practices into action, businesses can unlock the potential of LLMs while keeping their data safe and secure. It’s a smart way to gain insights without compromising on protection.
How can businesses reduce the risk of inaccurate or misleading outputs from LLMs in business intelligence?
To reduce the chances of inaccurate or misleading outputs (commonly referred to as hallucinations) from large language models (LLMs) in business intelligence, companies can implement a couple of practical strategies:
Rigorous testing and validation: Regularly compare LLM outputs against trusted benchmarks and perform regression tests after updates to confirm consistent accuracy. This helps identify and address potential issues early.
Human oversight: Involve skilled data analysts to review and verify the insights generated by LLMs. Their expertise ensures that decisions are backed by accurate and reliable data.
By blending careful testing with expert human review, businesses can improve the dependability of LLM-driven analytics and make more confident decisions.
What ethical and compliance factors should businesses consider when using LLMs in their operations?
When integrating large language models (LLMs) into business operations, it's essential to tackle a few critical ethical and compliance challenges head-on.
Data privacy should always be at the forefront. Businesses must ensure sensitive information is securely managed to avoid breaches and protect customer trust. This includes adhering to key regulations like GDPR or HIPAA, which are designed to safeguard personal data and uphold transparency.
Another major concern is algorithmic bias. LLMs, while powerful, can unintentionally produce biased results, which might lead to unfair or discriminatory outcomes. To address this, businesses need to rigorously test and refine their models, ensuring their AI processes remain as fair and impartial as possible.
Finally, there's the issue of accuracy and reliability. LLMs aren't infallible - they can sometimes produce misleading or incorrect information. To counter this, companies should establish robust governance frameworks and ethical guidelines, ensuring AI-driven insights are both trustworthy and responsibly applied in decision-making processes.