Generative AI has taken over the world, with use cases spanning various industries and departments expected to result in a market volume of $356.10 billion by 2030. From Copilots to Agentic AI or custom LLMs, many companies are leveraging Gen AI tools to improve efficiency, reduce manual effort, and keep pace with the latest tech trends. However, not all Gen AI implementations are successful. 

In March 2024, Microsoft-powered chatbot MyCity gave entrepreneurs incorrect information on starting and operating businesses in the city, which would lead to them breaking the law. 

According to a September 2023 report, a Microsoft employee accidentally exposed 38 terabytes of private data while publishing a bucket of open-source AI training data on GitHub. 

In August 2023, tutoring company iTutor Group’s AI-powered recruiting software automatically and unlawfully rejected female applicants ages 55 and older and male applicants ages 60 and older. 

Incidents like these highlight the need to ensure ethical AI practices when training generative AI models, especially when sensitive enterprise data is involved. Organizations must be aware of the risks involved in training models and integrate the right tools and approaches to ensure responsible usage and accurate outcomes. 

Gen AI Data Security and Privacy Risks 

Generative AI innovations like Agentic AI can improve a company’s efficiency, innovation, and competitive advantage. However, they can also introduce challenges and risks. Data security and privacy risks can lead to several far-reaching consequences, from risky user prompts to inaccurate content generated by LLMs, including hallucinations, inappropriate content, and misinformation. Let’s look at some of the data security and privacy risks of Gen AI: 

• Unauthorized access or distribution of sensitive content: Generative AI systems can create content automatically based on human prompts, which can be used for harm, intentionally or unintentionally. Unauthorized access or distribution of sensitive content can have several repercussions, including identity theft, financial loss, reputational damage, legal consequences, and a breach of privacy.

• Insufficient training data: As AI tools become more innovative and intelligent, they still lack intelligent behavior. While learning from large datasets, models can unknowingly mirror unconscious biases in human behavior and society. At the same time, insufficient training data can also lead to biased outcomes. For example, training AI models with demographic data of Americans cannot produce accurate results when used to conclude Asians – leading to bias.  

• Copyright issues: Generative AI tools are trained on massive databases from multiple internal and external sources. Utilizing unknown data sources can lead to reputational and financial risks. For example, a Gen AI model could produce a product idea based on another company's intellectual property. 

• Malicious intent: When organizations input sensitive data intoGen AI models, it can cause threat actors with malicious intent to generate convincing phishing emails, social engineering messages, and other AI-generated text that bypass traditional security measures. Publicly-hosted models are at a higher risk, as there is little to no insight into what security protocols are in place.

• Data privacy violations: Training large language models with personally identifiable information (PII) about individuals can lead to data privacy violations, which can cost millions of dollars in fines and non-compliance fees. Organizations must constantly ensure PII isn't embedded in LLMs while also quickly removing PII from models in compliance with privacy laws.

Ensuring Ethical AI Practices – Tips

As companies increase their reliance on Artificial Intelligence (AI), there is a strong ethical imperative that transcends sectors. How do you ensure fairness and trustworthiness in AI systems? How do you mitigate bias and discrimination? How do you protect individual privacy when interacting with data-intensive operations? How do you safeguard against misuse and wrong intent?

Here are some ethical AI practices to keep in mind: 

• Establish clear accountability frameworks and regulations: One of the best ways to ensure transparency and accountability in Gen AI adoption is by developing regulatory frameworks. Organizations must work with ITteams to establish accountability for platform governance, including defining boundaries within the data and AI framework and ensuring buy-in from the AI solution provider, company, and every stakeholder involved. 

• Train models with diverse, relevant data: Training Gen AI platforms with diverse data models is vital to improving their effectiveness and minimizing bias. Diverse data can mitigate unfair outcomes, enhance generalization, and improve model accuracy through exposure to richer and holistic data sets. When AI tools mirror the diversity found in practical applications, they can promote fairness and reduce social inequalities. Organizations must also constantly evaluate AI systems against known benchmarks to detect disparities. They must implement techniques that adjust algorithms to guarantee outcomes remain the same even if sensitive attributes differ.

• Anonymize/tokenize sensitive data: When sensitive enterprise data is involved, anonymization and tokenization are effective ways to ensure privacy and protection. Anonymizing personal data so individuals cannot be identified directly or indirectly and substituting sensitive data elements with non-sensitive equivalents or tokens, Gen AI systems can perform operations securely without hassle. 

• Identify and protect personal identifying information (PII): Companies in industries like banking and healthcare must also identify and protect any personal identifying information (PII) present in the data used for training. Data anonymization, encryption, data masking, role-based access control, and multifactor authentication are some ways to prevent identity theft, privacy breaches, and unauthorized access to sensitive personal data. 

• Set guidelines for responsible use of AI data: Organizations that feed sensitive enterprise data to Gen AI models must set strict guidelines for responsible use. Company-wide guidelines can help establish clear accountabilities and transparency requirements while defining permissible application areas and purposes and identifying the risk potentials of such systems. 

• Monitor and track data and interactions: Organizations must enable more substantial transparency obligations and establish regulations on the information used as training data. They must also monitor and track data and interactions, establish internal roles and processes for accountability, and share necessary information with stakeholders to ensure compliance. 

• Train and educate users on AI ethics: Training and educating users on the opportunities and risks that can arise when applying Gen AI is also critical to successful adoption. Since the wrong use of Gen AI outputs can result in reputation loss, legal issues, or security concerns, Gen AI hygiene should be considered seriously. Users should receive training on quality improvement opportunities, such as prompt engineering, to understand the need for independent verification and the dangers of publishing sensitive information. 

The Journey Forward 

As companies embrace AI (and more recently Agentic AI) for quality control, workforce productivity, and process improvement, ethical considerations must guide the journey forward as we stand at the crossroads of AI innovation. Enforcing accountability, setting up guardrails, and promoting transparency can go a long way in addressing privacy concerns, minimizing biases, and fostering human-AI collaboration. 

Are you ready to embark on the Gen AI journey ethically and responsibly?