Address top AI privacy concerns with this 7-point checklist

6 top AI privacy concerns

AI systems rely on large data sets that often contain personally identifiable information -- such as customer contact details and patient records -- or proprietary information like internal documentation. Mishandling this data can result in breaches, ethical violations, diminished user trust and regulatory penalties.

The following six privacy concerns are especially critical for businesses to address.

1. Data breaches

AI systems often process sensitive information and touch critical workflows, making them prime targets for threat actors. A single data breach can expose millions of records, leading to identity theft, financial loss and reputational damage. For instance, a healthcare AI system breach could reveal patient diagnoses, treatment plans and billing information, affecting thousands of individuals.

2. Data misuse

Developing an AI tool involves many stakeholders, including software development teams, data engineers and third-party vendors. This widespread access during tool development increases the risk of misuse -- for example, reusing private training data for unauthorized purposes, such as selling it to advertising companies for customer profiling.

3. Black box models

Many AI systems operate as black boxes, meaning that, while users can see the input and output, the model's internal logic, training data and algorithms remain hidden. This lack of visibility makes it difficult to audit AI tools or understand how they make decisions. In some cases, AI vendors are also able to access data processed using their tools, potentially revealing sensitive information to unintended parties.

4. Lack of transparency

Users often don't know how AI models handle their data, as organizations don't always provide explicit or clear information about how they collect and use data for AI. In one high-profile case from 2016, Google-owned DeepMind received access to the healthcare data of 1.6 million patients -- who didn't know that their data would be used to train AI models. This incident led to regulatory investigations and public backlash, damaging trust in the healthcare provider and the technology company.

5. AI bias

AI systems trained on biased data, whether public or private, are more likely to produce biased outcomes. Adversarial attacks against AI models can also lead to biased decision-making by injecting poisoned data into training sets. Biased AI models might make discriminatory decisions in lending, hiring or healthcare based on factors such as gender, race or socioeconomic status.

6. Compliance risks

Some countries impose strict data protection regulations to secure citizens' personal information. For example, the GDPR, CCPA and China's Personal Information Protection Law establish strict rules around personal data use. Failure to comply with these regulations can lead to substantial financial penalties and reputational harm.

Checklist: 7 best practices for AI privacy in business

To address privacy concerns and use AI safely in business environments, organizations should adopt the following seven privacy best practices.

1. Collect minimal data

Gather only the data necessary for the task at hand, and ensure that users provide explicit, informed consent regarding how their personal data and prompts are used to train AI models. For example, a business could design its customer service AI tool to process only specific query details without storing personal identifiers or conversation history.

2. Use encryption

Encrypt all communications between users and AI systems to prevent external observers from monitoring and intercepting data exchanges. In addition, encrypt data at rest -- including data sets for model training, backups and development environments -- to prevent unauthorized access. This includes implementing secure key management systems.

3. Anonymize sensitive data

To prevent user profiling, remove or mask personal, health, financial and other sensitive information using techniques like data masking, tokenization and differential privacy. Consider adopting these advanced privacy-preserving techniques to reduce risk while maintaining AI performance:

• Federated learning. Trains models across decentralized devices while keeping data local.
• Secure multi-party computation. Performs computations on encrypted data without exposing the underlying data itself.

4. Adopt explainable AI

Use explainable AI techniques, such as local interpretable model-agnostic explanations, that provide insight into how models make decisions by observing patterns in model outcomes. Explainable AI methods help mitigate risks associated with black box models while maintaining model performance and trustworthiness.

5. Check for compliance

Ensure that AI tools comply with applicable regulations, such as GDPR and CCPA. Conduct regular audits, maintain compliance documentation and communicate data collection practices clearly to users. These steps provide evidence of due diligence in the event of a regulatory inquiry.

6. Implement strong access controls

Restrict access to AI systems and their underlying data to authorized personnel. Implement role-based access controls and maintain detailed logs to track who accesses sensitive data and when. This reduces the risk of internal data misuse and creates organizational accountability.

7. Establish an AI governance framework

Develop a governance framework that defines roles, responsibilities and procedures for AI implementation. The framework should also outline an incident response plan with clearly defined escalation protocols to handle unexpected issues such as model errors or data breaches.

Lay out processes for data stewardship, such as enforcing encryption standards and maintaining data lineage for audits. Set ethical guidelines to avoid unfair outcomes, like biased credit scoring. To catch issues early, include risk assessment methods, such as regular model bias checks or adversarial testing.

Nihad A. Hassan is an independent cybersecurity consultant, expert in digital forensics and cyber open source intelligence, blogger, and book author. Hassan has been actively researching various areas of information security for more than 15 years and has developed numerous cybersecurity education courses and technical guides.