AI Privacy Explained: What You Need to Know About Data Security

“Is your personal information truly secure in the world of AI?”

Artificial Intelligence today is no longer a futuristic concept as it has made its way into almost every facet of our lives. From redefining business operations to strategic decision-making, AI’s ability to process massive volumes of data is transforming industries across the board. However, there is a dark side to it - AI privacy, as the volume of data processed grows exponentially.

In this blog, we will explore the essentials of data privacy in AI, best practices for protecting data, and highlight real-world examples that show why privacy is non-negotiable today.

What is AI Privacy?

AI systems usually rely on vast and diverse datasets to function and improve. They deal with both structured data (financial records and customer databases) and unstructured data (text, images, and audio). Semi-structured data (JSON files and XML records) are also processed by these systems, which utilize all three data types for tasks like prediction, classification, and pattern recognition. 

This is where AI privacy steps in. It is the ethical collection, storage, and protection of personal and sensitive data used by artificial intelligence systems. 

Why is AI Privacy a Critical Concern for Businesses and Individuals?

According to a recent KPMG report, it was revealed that only 46% of people globally are willing to trust AI systems (Source). While this can be a major roadblock for innovation and operational efficiency, it makes sense that industry leaders and teams may feel this way. AI privacy risks are on the rise, where businesses could easily be subject to legal penalties, financial loss, and reputational damage. And, the need for data privacy goes beyond just the protection of personal data:

Protection of fundamental human rights

Privacy is a fundamental human right. And in AI use, it entails full autonomy, freedom of association, and freedom of expression. AI systems that collect and use personal data without consent or adequate safeguards infringe on these rights, putting users through intrusive surveillance. 

Ensuring fairness and accountability in AI-driven decisions

Over the next three years, 92% of companies plan to increase their AI investments (Source). Therein lies the challenge: How can you ensure fairness and accountability when making AI-driven decisions? 

For starters, organizations may struggle to hold AI algorithms accountable for their data processing and decision-making protocols. AI models trained with datasets containing societal biases can perpetuate or amplify discrimination against marginalized groups. Instances of discrimination include unfair treatment in hiring and credit scoring. As a result, ethical AI development requires embedding privacy as a foundational principle to ensure fairness, reduce bias, and maintain accountability.

What Kinds of Data Concerns Are We Facing with AI Privacy?

AI systems and computers collect, process, and store different kinds of data. They can be both qualitative (survey responses, transcripts, and case studies) and quantitative (IoT device data, health records, and financial data). Biometric data is also a key data type collected, which is inherently numerical, but can also be used to interpret and understand user behavior, emotions, and preferences. But none of these data types are free of AI privacy risks. 

According to a recent HAI report, AI incidents jumped by 56.4% in a single year, with 233 reported cases throughout 2024 (Source). Organizations today struggle to combat such incidents, and with advancements in technology, come new and different kinds of AI security threats. Here are some of the rising data concerns that companies need to combat:

Insufficient data privacy plans 

The volume of data entering the digital sphere is growing at an unprecedented rate, and businesses contribute to that growth every time they are open and conduct operations online. But what happens when the business does not have the right digital infrastructure to handle and protect all its data? The more data they produce, store, and share, the more likely they are to encounter data privacy issues. 

Preventative software could address privacy concerns at scale and should at least account for the following:

• The number of users and their permissions throughout the network
• The company’s most critical and confidential data
• The volume of data stored physically and in the cloud
• Each employee’s average technology needs and usage

Data trading

Being one of the most insidious AI data privacy issues in the digital space, data trading involves third-party access, theft of confidential information, and sale of this information to other parties. Once data traders get hold of sensitive company or customer data, they can cause plenty of harm, such as:

• Identity theft - Hackers can impersonate businesses or customers online for personal gains like making unauthorized purchases, issuing electronic bank transfers from their accounts, or applying for loans under their name.
• Data hostaging - Data traders may invite individuals for negotiations by keeping their sensitive data hostage. If the individual doesn’t respond, they take offers from other bidders.
• Targeted advertising - Data traders can sell individual or business data to advertising companies, who then can create ads personally tailored to search engine results or digital shopping lists. 

Insufficient SOPs

Humans can still make mistakes despite setting up the best data privacy measures. As such, businesses can’t rely on just software for data privacy and security in AI. They’ll have to develop standard operating procedures (SOPs) that need to be followed. This may include:

• New device setup and privacy protection
• Training new employees to access and follow SOPs
• Fine-tuning them after every change or update made in the data privacy software
• Enforcing review standards based on when, why, how, and by whom
• Strictly adhering to document naming and filing conventions

Location tracking

Through location tracking, hackers can cause chaos in business operations. They can infiltrate the location data of employees to unlock or sell trade secrets, confidential customer data, supply chain data, and business development plans.

For example, by hacking into an employee’s location data while they meet suppliers and make deliveries, hackers can uncover details like:

• Primary material suppliers and consulting partners
• Retail and individual clients
• Vehicle storage times and locations after business closing hours

With access to such information, data traders could sell it to competitors, jeopardizing the business’s operations. 

The existence of such data concerns calls for swift action through the deployment of robust security measures. And for companies that heavily rely on AI systems, there’s also the issue of compliance and regulations they’ll need to keep in mind. This is where privacy laws for AI usage enter the fold.

AI Privacy Laws Around the World We Need To Know About

Did you know that there is a public mandate for national and international AI regulation, with 70% believing regulation is needed (Source)? It’s no secret that AI brings its unique advantages and disadvantages. But when its use is properly monitored and regulated, companies can exploit its full potential with little to no negative impact. The first step to achieving this is by building awareness of AI privacy laws around the world that govern the use of this evolving technology.

General Data Protection Regulation (GDPR)

Enforced since May 25, 2018, the GDPR is a European Union law that primarily focuses on data protection and privacy. Its main objectives are to enhance individuals’ control and rights over their data and information, and regulate how companies use it. The law also mandates fair and transparent processing of personal data with clear purpose limitation and data minimization. 

GDPR non-compliance can result in severe penalties, including fines imposed on a company’s global annual turnover. These penalties are tiered under two categories:

• Lower Tier - Up to 10 million euros or 2% of annual turnover, whichever is higher

• Higher Tier - Up to 20 million euros or 4% of annual turnover, whichever is higher 

(Source)

California Consumer Privacy Act (CCPA), USA

Signed into law in June 2018, the CCPA grants California residents the right to control their personal information held by businesses. The act secures consumer privacy rights, such as the right to:

• Know about the personal information businesses collect about them
• Delete personal information collected from them
• Opt out of the sale or sharing of personal information
• Non-discrimination for exercising their CCPA rights 
• Correct inaccurate personal information that a business has about them
• Limit the use and disclosure of sensitive information collected about them

Businesses that are subject to the CCPA also have responsibilities like promptly responding to consumer requests to exercise their rights and sending them notices explaining their privacy policies. Deviations from these responsibilities and any other violations will result in penalties with fines reaching up to $7,988 for each intentional violation and $2,663 for each unintentional violation (Source).

Digital Personal Data Protection Act (DPDPA), India

Brought into effect in 2023, the DPDPA is India’s first-ever comprehensive law focused on protecting individuals’ digital personal data. It establishes a framework on how businesses should regulate the collection, storage, and processing of personal data, ensuring individuals have control over their information. The act also outlines significant penalties for breaches of data privacy. Here’s a breakdown:

• Failure to implement reasonable security safeguards = Up to Rs. 250 crore
• Delayed breach reporting and violating children’s data provisions = Up to Rs. 200 crore
• For individuals who file frivolous/false complaints = Up to Rs. 10,000 
• Breaches of any other provisions of the DPDP Act or its rules = Up to Rs. 50 crore

(Source)

European Union AI Act 

Brought into force on August 1, 2024, the EU AI Act is a comprehensive act designed to regulate the development and use of AI within the EU. It establishes a risk-based approach, classifying AI systems into four risk levels: unacceptable risk, high risk, limited risk, and minimal risk.

The act also includes provisions for general-purpose AI models, like those used for language processing, and bans certain AI practices like social scoring systems. Any violation can reap severe penalties depending on the severity and nature of the infringement.

• For prohibited practices or data-related violations – Up to 35 million euros or 7% of annual turnover, whichever is higher.
• For non-compliance with requirements of high-risk AI systems – Up to 15 million euros or 3% of annual turnover, whichever is higher.
• For misleading or incorrect information – Up to 7.5 million euros or 1% of annual turnover, whichever is higher.(Source)

Non-compliance with the above regulations will put businesses through heavy financial burdens while compromising the safety and privacy of individuals and their data. But there are ways to avoid such penalties and maintain the integrity of your AI systems. 

AI Privacy Best Practices

Building on the lessons learned from privacy breaches due to AI and what happens when you don’t comply with regulatory standards, your next step is to develop robust measures to safeguard sensitive data and maintain user trust. That said, here are some of the best practices you can follow to mitigate AI security risks:

Establish an AI data privacy and compliance team 

This is a foundational step in responsible AI governance. You’ll need to set up a multidisciplinary team consisting of legal experts, data scientists, and professionals who’ll ensure AI initiatives comply with data protection laws and ethical standards. Their role includes defining clear responsibilities, monitoring compliance, and staying updated on evolving regulations to proactively address potential AI privacy risks. 

Enforce clear AI privacy and governance policies

Privacy should be embedded by design into AI development, with clear governance policies that articulate rules around data collection, storage, and sharing. These policies should also ensure alignment with local and global privacy regulations, covering technical measures like data encryption, anonymization, and access controls. Employee training may also be a part of the process, helping build safeguards throughout the AI lifecycle. 

Conduct privacy impact assessments (PIAs) 

By conducting PIAs regularly, you can identify and manage privacy risks arising from new projects, systems, policies, and strategies. They evaluate how personal data is handled, assessing potential risks to individuals’ privacy, and recommend measures to mitigate them. The implementation involves 4 key steps:

• Project initiation – Defines the scope of the PIA process depending on the stage of project initiation.
• Data flow analysis – Maps out how the business will handle personal information and determines how it flows through the organization as a result of business activities. 
• Privacy analysis – Personnel involved in the data flow analysis may complete privacy analysis questionnaires, followed by reviews and discussions of privacy issues. 
• PIA report – Privacy risks and implications are documented, followed by discussions of possible efforts to remedy the risks. 

Limit data collection

Also known as data minimization, this is where you limit the collection of training data to what can be collected lawfully and used according to the expectations of people whose data is collected. By avoiding the accumulation of irrelevant or excessive personal data, you avoid the risk of data breaches and simplify compliance efforts. You also maintain and respect user privacy, which is key to building trust and transparency when using AI solutions to handle various data types. 

Real-World AI Privacy Breaches: Insights from Key Privacy Failures

With AI technologies being integrated into everyday business operations and consumer interactions, there have been several high-profile incidents exposing vulnerabilities in AI data privacy. These real-world breaches have taken place in different forms, highlighting the urgent need to address such privacy failures. Let’s look at some major incidents that have happened: 

AI Bias in Aon’s Hiring Software 

Back in May 2024, the ACLU filed a complaint with the U.S. Federal Trade Commission against Aon’s AI-powered hiring tools that were marketed as “bias-free,” but actually discriminated against job candidates based on race and disability. The complaint specifically targeted Aon’s personality assessment test and AI-automated video interviewing tool, which were marketed to recruiters as an efficient, cost-effective, and less discriminatory alternative. (Source)

The ACLU argued these tools assessed general personality traits like positivity and emotional awareness, which were not directly related to job requirements. They unfairly screened out qualified candidates linked somehow to various mental health issues like depression and autism. AI used in the video interviewing tool likely discriminated based on race or disability, further exacerbating the problem with biased characteristics being unfairly scrutinized.

While not directly connected to AI privacy issues, this case study reflects the non-transparent decision-making process, which questions approaches to AI bias and fairness. 

Italy fines the city of Trento for AI violations 

In January 2024, the Italian city of Trento was fined 50,000 euros by the Italian Data Protection Authority (GPDP) for violations related to the use of AI in two urban surveillance projects funded by the EU. The projects involved AI-driven analysis of video, audio, and social media data to detect crimes in urban areas. However, the GPDP found that Trento broke multiple data protection rules. Some of the key issues included:

• Personal data (including conversations recorded by street microphones) was not anonymized properly and was circulated among third parties, especially project partners.

• The surveillance and data processing were considered invasive, with no impact assessment conducted.

• The city failed to inform the public about the nature and extent of data processing involved.

(Source)

Trento is the first local administration in Italy to be sanctioned by the GPDP over AI privacy violations, as a result of which the city was told to delete all data gathered in its EU-funded projects. 

Clearview AI’s facial recognition database

Clearview AI, a US facial recognition company, was slapped with a 30.5 million euro fine by the Dutch Data Protection Authority in September 2024. The company incurred an additional penalty of up to 5 million euros for breaching regulations, amidst non-compliance issues.

(Source)

Clearview AI defended its position, stating it neither operated nor had any customers in the Netherlands and the EU. The company also claimed not to undertake activities that would be subject to GDPR scrutiny. However, the DPA stated that Clearview’s services were illegal under Dutch regulations. 

Emerging Tech Innovations Strengthening AI Privacy and Protection

Advancements in technology are creating new avenues for enhancing AI privacy and protection. They not only bolster security but also empower organizations to build AI systems that respect user privacy by design. Here are some examples of emerging technology trends set to reshape data privacy and security in AI:

Development of Explainable AI (XAI) 

Explainable AI is becoming a key technology for ensuring fairness, transparency, and accountability in AI systems. And its market size in 2025 is expected to reach a valuation of $9.77 billion from $8.1 billion (2024), thanks to its steady adoption by businesses worldwide. (Source)

XAI primarily focuses on the “black-box” nature of AI models, making decision-making processes transparent and understandable to humans. This transparency builds trust, ensures accountability, and helps businesses comply with regulations, as stakeholders may also understand and challenge AI decisions when necessary. It also improves ethical AI development by detecting biases and ensuring fairness. 

Blockchain and decentralized data storage solutions

Blockchain provides users secure data storage and decentralized control over personal information. This tech stacks up with explainable AI by furnishing a highly secure distributed ledger resistant to tampering and records AI decisions with their rationales. Decentralized storage solutions built on blockchain usually keep sensitive data from being stored in a single location, thereby minimizing risk of breaches.

Privacy-enhancement technologies

Privacy-enhancing technologies (PETs) are a suite of tools and techniques that enable secure collection, analysis, and sharing of data while maintaining the privacy of individuals. A few examples of PETs include:

• Differential privacy – This is a framework for measuring and ensuring privacy in data analysis by adding carefully calibrated noise to data or model outputs. It aims to obscure personal details of individuals while preserving the integrity of statistical patterns. 
• Homomorphic encryption – This is a form of encryption where computations can be performed on encrypted data without the need for decrypting it first. It ensures sensitive data remains encrypted while processed, maintaining privacy and security.
• Zero-Knowledge proofs – These are cryptographic protocols that allow verification of AI model decisions without revealing underlying data. For example, a person can prove they are over a certain age without revealing their actual age. 

Staying Ahead of AI Privacy Concerns with Tredence

AI privacy stands at the forefront of protecting individuals and businesses from cyberattacks as AI influences nearly every aspect of our lives. The mass circulation of data across the internet has made way for data privacy issues like breaches, bias, unauthorized access and transparency issues. By adhering to best practices discussed above, you not only comply with regulations, but also build trust and confidence among users and stakeholders. 

If you’re a business looking to excel in AI privacy and data security, Tredence stands out as your ideal AI consulting partner. Because we don’t just protect data, we protect decisions, reputations and the trust you place over your AI systems.

With our expertise and AI-powered tech stack, we help you build secure, scalable and responsible AI solutions. Partner with us today and build secure AI systems you can trust. 

FAQs

1. What are common data privacy concerns with generative AI?

Common privacy concerns with generative AI include lack of transparency, unauthorized data sharing, data leaks, and inadequate anonymization. 

2. How do AI systems ensure data privacy and security?

AI systems enable real-time threat detection and analysis of enormous volumes of data. They automate responses to attacks, limiting the damage dealt to resources and individuals within the organization. They also detect suspicious behavior, preparing measures early on to tackle security threats. 

3. How do companies anonymize data before training AI?

Companies employ various techniques to anonymize data before training AI models, primarily to protect sensitive information and comply with privacy regulations. These methods focus on transforming or removing personally-identifiable information (PII) so individuals cannot be re-identified. A few anonymization techniques include data masking, data swapping, synthetic data generation, and pseudonymization.

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Editorial Team
Tredence