Artificial intelligence (AI) makes work easier and quicker for anyone who cares to leverage the technology. But what some users don’t know if AI can infringe on Intellectual Property (IP) rights, exposing sensitive data and information to the public.
Do you frequently use AI in your day-to-day processes and worry about AI privacy violations? This post discusses AI and data protection and how you can escape artificial intelligence privacy issues.
Is it safe to use AI despite privacy issues?
Using AI is becoming a norm across multiple industries, and many organisations are integrating AI into their everyday processes. But just like any other thing at its early developmental stage, AI has issues many people are concerned about.
However, we recommend that you learn how to leverage the capabilities of AI tools for your projects while still ensuring your data is protected. In our opinion, the benefits of using AI outweigh the challenges. Moreover, the issues relative to privacy can be fixed.
Start by reviewing your own processes and define an AI policy on a company level that is plain and simple to use by every employee.
Then, look into the approach of individual tools in terms of how they are interacting with the user data. Very importantly, you should learn the best practices to safeguard your data while using AI to complete your projects.
Savvy users are opting for additional layers of security on the client side that works as an IP protector. Many companies are using this to install this extra layer of protection on their infrastructure (on AWS, for example). This layer has access to the company IP and doesn’t share it with the AI in the cloud. Hence, the AI can access public data and the internal IP without breaking the IP protection. Some tools also have options to be installed “in-house” and don’t share any data, and there are also options to “disable” data training and information sharing (with restrictions).
Continue reading to learn how you can deal with privacy issues when using AI tools.
Tips for Validating AI Tools & AI privacy compliance
Discover what can help you in understanding the intricacies of different AI tools and what type of best practices and strategies can strengthen your AI policies.
- Understanding data protection principles: Organisations should comply with principles such as fairness, transparency, purpose limitation, data minimisation, accuracy, storage limitation, and security under GDPR.
- Explaining AI and ML algorithms: Transparency about AI and ML algorithms is essential to help individuals understand how their data is being used.
- Minimising data: Collect and process only the minimum amount of data necessary for the intended purposes.
- Ensuring accuracy: Take steps to ensure the accuracy of the data used in AI and ML algorithms.
- Ensuring security: Implement appropriate security measures to protect collected and processed data and the algorithms used.
- Respecting individuals’ data protection rights: Ensure that individuals’ data protection rights are respected, including the right to access, rectify, and object to processing.
- Conducting a Data Protection Impact Assessment (DPIA): Assess risks associated with AI and ML processes and identify mitigation strategies.
- Privacy by Design (PbD): Embed privacy and data protection into the design and development of systems, processes, and products proactively.
What are the tools for ensuring AI privacy compliance?
- Data masking and anonymization tools: These tools help protect personal data used in AI models by removing identifying information from the data, reducing the risk of unauthorised access and data breaches. Examples are VGS Platform, LiveRamp, Immuta, and Oracle Data Masking and Subsetting.
- Encryption tools: Encryption tools protect data in transit and at rest, preventing unauthorised access to sensitive data and enhancing privacy compliance. Examples are VeraCrypt, TrueCrypt, DiskCryptor, and BitLocker.
- Access control and audit tools: Access control tools ensure that only authorised individuals or processes can access personal data used in AI models. Additionally, they help monitor and track access to data, detecting and addressing any unauthorised access.
12 Best practices to protect your IP while using AI
To avoid IP issues and exposing your data when using AI, we recommend the following practices:
1. Register for AI-Specific IP:
Protect AI algorithms and models by considering patents or keeping them as trade secrets. Ensure documentation for AI inventions is robust to support patent applications.
2. Use Advanced Privacy-Enhancing Technologies:
Adopt AI-specific techniques like differential privacy or homomorphic encryption to safeguard data used by AI models.
3. Perform AI Model Security Audits:
Conduct regular security audits focusing on AI model integrity, verifying that models are protected against adversarial attacks and unauthorized data access.
4. Enhance AI-Specific Employee Training:
Train employees on the nuances of AI data protection, emphasizing responsible AI development and deployment practices that safeguard IP.
5. Implement AI Data Access Controls:
Leverage AI-based systems to control access to data, ensuring that only authorized AI models or personnel can interact with sensitive information.
6. Set Clear AI Data Retention Policies:
Clearly define how AI systems store data, specifying retention periods and ensuring compliance with relevant regulations to protect IP.
7. AI Model IP Restrictions:
Define clear IP restrictions and obligations related to AI model sharing and usage to prevent unauthorized distribution or modification.
8. Pursue AI-Focused NDAs:
Draft NDAs that specifically address the use of AI models or algorithms, ensuring collaborators understand their obligations regarding proprietary information.
9. Monitor AI Implementations for IP Compliance:
Use AI-driven monitoring tools to track third-party AI implementations, ensuring compliance with your IP policies and identifying any potential breaches.
10. Secure AI Data Transmission:
Implement secure protocols for data transmission to and from AI systems, using encryption technologies specifically suited for AI datasets and models.
11. Regular AI IP Audits and Inventorship Tracking:
Conduct audits specifically focused on AI innovations to ensure proper inventorship tracking and protection of novel AI developments.
12. AI-Centric Data Governance Policies:
Develop comprehensive data governance frameworks that focus on the unique aspects of AI, ensuring proper management and protection of data used by AI systems.
13. Monitor third-party AI implementations
Integrating AI solutions from third-party vendors can enhance operational capabilities, but it also introduces potential IP risks. Ensure that the vendors you collaborate with have a track record of complying with data protection regulations.
Review licensing agreements and terms of service to understand how your data and IP will be handled. Also, implement regular monitoring and auditing procedures to ensure the third-party AI implementation remains secure and in compliance with your agreements.
How to avoid AI tools with privacy issues
Before using AI for your projects, find out the approach and policies such AI tools have relative to interacting with your data. AI tools that prioritize IP rights and user data privacy will:
1. Practise prudent data hygiene
Reliable AI systems typically adopt a data collection approach that priorities necessity. That is, they only gather the data types essential for AI development and ensure its security. Besides, these tools only retain data for as long as required to fulfil the intended purpose.
2. Employ quality data sets
When building AI, developers concerned about data privacy utilise accurate, unbiased, and representative data sets. Additionally, they tend to create AI algorithms that can audit and assess the quality of other algorithms.
3. Empower user control
Data transparency and user consent are crucial, and developers ought to prioritise this in developing AI systems. AI developers must ensure users are informed when their data is being utilised, especially in AI-driven decision-making processes or AI creation. It is essential to provide users with the choice to give consent for data usage.
Conclusion
Artificial intelligence is incredibly helpful across many industries, including the software development industry. But since many AI tools are still in their development stage, you may experience data privacy and IP protection issues.
Yet, you can avoid being a victim by following our tips in this article. Use data integrity tools to check an AI’s adherence to data and privacy policies. Also, find their approach to data management, and follow the best practices in this guide.
We offer technology consulting services with security in mind, with more than ten years of helping partners build products they love. We are ISO27001 certified and our clients trust us to help them protect their IP rights and prevent their sensitive data from being exposed to third-party agents. Reach us now to learn how we can help you leverage AI without compromising data privacy and IP rights!
