The US has increased scrutiny on AI, building off President Biden's Executive Order, which mandates federal agencies to focus on responsible AI development. Simultaneously, the EU is on the brink of passing the AI Act, which will further regulate AI technologies in Europe.
These developments are part of a broader trend towards more stringent AI regulations globally, aiming to balance technological innovation with ethical considerations and privacy protection.
So, let’s learn how you can ensure secure usage of AI in your organization while staying compliant with global regulations.
Understanding AI privacy concerns
#1 Data misuse and lack of consent
The 2023 Pew Research Center survey found that 72% of Americans are concerned about how companies collect and use their personal data.
The core of AI privacy issues often begins with data misuse. AI systems need vast amounts of data to learn and improve. However, the way this data is collected can be dubious. Many times, personal information is gathered without clear consent or proper understanding from the users.
It's like someone secretly taking notes about your life—where you shop, what you like, your online searches—and using it to make a profile about you. This is not just a breach of trust but a significant privacy invasion. People should have the right to know and choose what information about them is being collected.
#2 Enhanced surveillance capabilities
By 2025, the global facial recognition market is projected to reach $9.8 billion.
Technologies like facial recognition and gait analysis allow constant monitoring of individuals. This isn't limited to high-security areas; but also in everyday spaces like shopping malls and streets.
It feels like there’s always an invisible eye watching, analyzing, and recording your every move. This level of surveillance can easily be misused by governments or corporations to track and control individuals, posing a stark threat to personal freedom and privacy.
#3 Profiling and discrimination
Here's a bitter truth – AI can be biased. When AI systems are fed data that reflects societal biases, they can inadvertently perpetuate discrimination. This could manifest in various ways, like job recruitment AI favoring a particular gender or race, or credit scoring algorithms unfairly biasing against certain socioeconomic groups.
Individuals are often unaware that their data is being used to make biased assessments about them, which can have real-life negative consequences.
#4 Opacity and lack of control
AI systems can be like black boxes—complex and opaque. The lack of transparency in how these systems work and make decisions is a significant privacy concern.
People are often in the dark about what data about them is being used and how. This lack of understanding and control over personal data is unsettling. Imagine not knowing why you were denied a loan or flagged at an airport, all because of an AI system’s hidden workings.
#5 Data breaches and security risks
“My team started using ChatGPT right away, which I think is a good thing in order to stay competitive. But they were putting in data that were honestly kind of horrifying. I was caught a little flat-footed, which is unfortunate, so I would say if you don’t have a policy, put one in.”
~Noga Rosenthal, General Counsel and Chief Privacy Officer, Ampersand
SpotDraft Panel and Q&A: Developing and Implementing Practical AI-Use Policies
AI systems are not impervious to cybersecurity threats. The more data an AI system holds, the more tantalizing a target it becomes for cyberattacks. Personal data stored in these systems can include sensitive information such as social security numbers, financial records, or health history.
A breach in AI data security could lead to massive privacy violations and identity theft. The risk of such breaches adds an extra layer of concern regarding how data is stored, protected, and used in AI systems.
6 Strategies to mitigate privacy risks associated with AI
Let’s look at some good ways to do this.
#1 Develop a comprehensive AI use policy
The first step is to draft a clear policy that governs the use of AI within the organization. This policy should outline what is permitted and what is not, focusing on ethical use, data protection, and privacy. It should serve as a guideline for all employees to understand the boundaries and expectations when working with AI. This policy should address:
- Data governance: How data is collected, stored, accessed, and secured for AI purposes
- Model explainability: Ensuring transparency and understandability of how AI models arrive at decisions
- User consent: Obtaining informed and meaningful consent for data collection and use in AI systems
- Risk management: Identifying and mitigating potential privacy risks associated with specific AI projects
#2 Conduct privacy impact assessments (PIAs)
PIAs are your best friend here, and are a critical tool for identifying potential privacy risks associated with AI projects. By regularly conducting PIAs, you can identify potential privacy issues before they become real problems. Conduct these assessments during the planning stage of any project involving personal data and revisit them regularly as the project evolves.
A PIA involves a detailed analysis of how data is collected, processed, stored, and deleted, identifying risks to privacy at each stage. It also requires evaluating the necessity and proportionality of data processing, ensuring that only the minimum amount of data necessary for the project's objectives is used.
#3 Ensure transparency and consent
“Tell people what you are doing with their personal data, and then do only what you told them you would do. If you and your company do this, you will likely solve 90% of any serious data privacy issues.”
~Sterling Miller, CEO of Hilgers Graben PLLC
Ten Things: Data Privacy – The Essentials
Transparency is key. You need to ensure that clear, concise information is provided to users about the AI systems in use, the nature of data being collected, and how it will be used. This information should be presented in a user-friendly manner, avoiding technical jargon that could obscure understanding. Securing informed consent is equally critical; your customers should have a clear choice regarding their data, including the ability to opt-in or opt-out of data collection practices.
- Invest in explainable AI (XAI): Leverage XAI techniques to understand how AI models arrive at decisions, increasing transparency and accountability for potential algorithmic bias
- Communicate openly: Communicate transparently about the use of AI systems, their potential impact, and limitations, while balancing transparency with legitimate business interests
#4 Implement robust data security measures
Protecting the data that AI systems use is non-negotiable. You must work closely with IT and cybersecurity professionals to ensure that personal data is protected against unauthorized access, disclosure, alteration, and destruction. This includes employing encryption, implementing strong access controls, and regularly updating security protocols to address emerging threats.
- Regular security assessments: Engage third-party security experts to conduct penetration testing and vulnerability assessments to identify and address potential weaknesses in your defenses
- Stay vigilant: Continuously monitor evolving cyber threats and vulnerabilities. Subscribe to security advisories and updates to stay informed and adapt your security measures accordingly
- Compliance is key: Ensure your data security measures align with relevant data privacy regulations like GDPR and CCPA. Compliance not only minimizes legal risks but also demonstrates your commitment to responsible data handling
#5 Stay updated on regulations and standards
The regulatory landscape for privacy and AI is constantly changing, with new laws and guidelines emerging as technology evolves. You must remain vigilant, staying informed about current and upcoming privacy laws and regulations at both the international and local levels. Continuous education and adaptability are key, as is the ability to interpret how these regulations apply to your organization's specific use of AI.
- Track legal changes: Monitor global privacy laws and AI regulations
- Assess impact: Evaluate new regulations' effects on AI use
- Adopt standards: Follow international AI development standards
- Influence policy: Participate in regulatory discussions
- Join Legal groups: Engage with Legal networks for insights
- Use RegTech: Apply technology for compliance tracking
- Update programs: Regularly refresh compliance strategies
- Promote compliance culture: Foster organizational awareness and adherence
#6 Foster a culture of privacy
Promoting a culture that values and respects privacy is perhaps one of the most effective strategies for mitigating privacy risks.
- Develop AI literacy programs: Train all the different teams in the organization on:
1. Responsible AI practices and data privacy regulations
2. Their roles in upholding data protection principles
- Promote continuous learning: Encourage ongoing learning and awareness campaigns to keep pace with evolving AI technologies and the legal landscape
- Foster a privacy-conscious culture: Cultivate a culture where privacy is a shared responsibility, and everyone involved in AI projects understands their privacy obligations
New AI developments and privacy challenges
As AI gets smarter and does more, it's going to handle a lot more personal information. But this could mean more chances for privacy problems. The more AI knows, the more we need to be careful about keeping information safe. We'll need to be extra careful to keep our information private.
Enhanced predictive analytics
- Privacy risk: As AI's predictive capabilities advance, it'll start to infer personal information with greater accuracy, potentially unveiling sensitive details without explicit consent
- Mitigating the risk: Advocate for the development and implementation of AI models that prioritize data minimization and anonymization. Regularly reviewing and updating data protection policies to cover inferred data will also be key
Expansion of AI in decision-making
- Privacy risk: AI systems are increasingly involved in decision-making processes that affect individual rights, such as employment or credit scoring. This raises concerns over transparency and accountability
- Mitigating the risk: Ensure that AI systems used for decision-making are auditable and comply with fairness regulations. Work on establishing clear guidelines for AI's role in decisions, emphasizing the need for human oversight
Proliferation of IoT devices
- Privacy risk: The spread of Internet of Things (IoT) devices means more personal data collection at an unprecedented scale and granularity, heightening the risk of unauthorized access and data breaches
- Mitigating the risk: You should push for robust security standards and privacy-by-design principles in IoT development. Implementing strict access controls and data encryption is crucial to protect the information collected by these devices
Advances in Natural Language Processing (NLP)
- Privacy risk: Improvements in NLP enable AI to understand and generate human-like text, potentially leading to the misuse of sensitive information or creation of convincing phishing attempts
- Mitigating the risk: Developing comprehensive policies on the use of NLP technologies, including guidelines for data handling and user consent, is essential. Regularly train your staff to be able to recognize and protect against AI-generated phishing threats
Keep AI risks at bay with SpotDraft’s AI Policy Playbook
Understanding laws like GDPR and CCPA, and using smart strategies, is key to managing privacy risks in AI. As technology keeps advancing, we need to keep up and stay proactive.
One big help? Creating an AI use policy.
And if you're wondering how to start, SpotDraft’s AI Use Policy Playbook is just what you need. It gives you a good start to create a tailored policy for your organization, ensuring you're well-equipped to tackle privacy risks head-on.