Data Privacy Week: the role of AI in privacy
- Data privacy is a complex and evolving issue that affects individuals, organizations, and society as a whole.
- Data Privacy Day is an international event that occurs every year on 28th January.
- While AI can enhance data privacy, it also comes with challenges and risks
Data Privacy Day is an international event that occurs every year on 28th January. This year, Data Privacy Week takes place from the 21 to the 27th of January. The purpose of Data Privacy Week is to raise awareness and promote privacy and data protection best practices.
Over the years, data privacy has seen significant growth, especially with all businesses and governments prioritizing its importance. Data privacy laws also continue to evolve, especially with emerging technologies and the surge of data being generated around the world.
The EU’s GDPR remains the most powerful law in the world when it comes to data privacy. Governments around the world today implement new privacy laws and regulations based on the standards set by the GDPR. However, with the rise of generative AI use cases around the world, there are now calls for stronger data privacy laws to address them.
Once again, the EU is hoping to lead the regulations for AI. Given that AI will not only impact data privacy, the law will also need to cover the protection of intellectual property as well as ensure the technology is not used for the wrong reasons.
According to Arun Kumar, regional director at ManageEngine, safeguarding a company’s data is vital because it protects the organization from financial loss, reputational damage, and loss of intellectual property. Successful data protection requires a holistic approach where people, processes, and the technology framework are the focus.
The challenges to data privacy
Data privacy is a complex and evolving issue that affects individuals, organizations, and society as a whole. Some of the biggest problems of data privacy today are:
- Data breaches: Data breaches are unauthorized access or disclosure of personal or sensitive data that can compromise the security, integrity, and confidentiality of the data. Data breaches can result from cyberattacks, human errors, system failures, or malicious insiders. Data breaches can cause financial losses, reputational damage, legal liabilities, and emotional distress for the victims. In 2023, the average cost of a data breach was US$4.45 million according to a report by IBM.
- Data localization: Data localization is the requirement or preference to store or process data within a specific country or region. Data localization can be motivated by political, economic, or legal concerns. Data localization can pose challenges for global businesses that operate across multiple jurisdictions and have to comply with different privacy regulations and standards.
- Privacy-enhancing computation: Privacy-enhancing computation (PEC) is a technique that protects data in use from being exposed or analyzed by unauthorized parties. PEC uses cryptographic methods to ensure that only authorized parties can access the data while it is being processed. PEC enables new applications and services that require data processing in untrusted environments, such as public cloud or multiparty data sharing.
- Facial recognition: Facial recognition is a technology that identifies or verifies a person’s identity based on their facial features. Facial recognition can be used for various purposes, such as security, authentication, surveillance, or entertainment. Facial recognition raises privacy concerns because it can collect and store biometric data without the consent or knowledge of the users. Facial recognition can also be inaccurate, biased, or manipulated.
These are just some of the major concerns about data privacy. Other concerns include the use of data by businesses to understand consumer behavior, often without users’ explicit understanding at the point of use. Last year, Meta was fined a record US$1.3 billion and ordered to stop sending European user data to the US. Google has also been fined by several European regulators on data privacy issues.
“Organizations that implement alerts within their systems can enhance awareness about security incidents. Solutions like security information and event management (SIEM) are critical for enterprises to proactively identify, manage, and neutralize security threats using AI and automation. Education is also key; every single employee should share the responsibility of safeguarding their company’s data by implementing and adhering to data protection policies and processes,” added Kumar.
The role of AI
There are concerns about the impact of data privacy with AI. This is because, for an AI use case to work best, it needs to train on data. And the data on which it trains can include personal data.
For example, when using AI to generate images, the model needs to do it by analyzing images on the web. It then generates a new image. This has led to problems like deepfake images, where real images and footage of people are easily manipulated and transformed into pornographic content.
Despite these concerns, some tech companies are also using AI to boost data privacy in various ways. These include:
- Privacy Concierge: AI systems work as a “privacy concierge” for the network infrastructure to identify, redirect, and process privacy data requests much faster when compared to doing it manually.
- Data Classification: AI is seen to be highly efficient in classifying data and managing it in an organized way. This can help reduce the risk of data breaches and leaks by applying appropriate security measures to different types of data.
- Sensitive Data Management: AI can help protect sensitive data from unauthorized access or misuse by using techniques such as encryption, anonymization, or differential privacy. These techniques can ensure that the data remains confidential while still allowing for its analysis or sharing.
- Data Security: AI can also help improve the security of data by detecting and preventing potential cyberattacks, such as malware, phishing, or ransomware. AI can also monitor and audit the activities of users and systems to identify any anomalies or violations of data policies.
It is important to note that AI is a powerful tool. While it can enhance data privacy, it also comes with challenges and risks. Therefore, it is important to develop and implement ethical and responsible AI practices that respect the rights and interests of data subjects and stakeholders. Some of these practices include:
- Transparency: AI systems should be transparent about their purpose, functionality, limitations, and outcomes. Users should be able to understand how their data is collected, processed, stored, shared, and used by AI systems.
- Accountability: AI systems should be accountable for their actions and decisions. Users should be able to hold AI systems responsible for any harm or damage they cause to individuals or society.
- Fairness: AI systems should be fair and unbiased in their treatment of different groups of people. Users should be able to challenge any discrimination or injustice caused by AI systems.
- Privacy by Design: AI systems should be designed with privacy in mind from the outset. Users should have control over their own data and how it is used by AI systems.
“Overall, data privacy is an essential part of any business, not only because it helps them comply with data privacy regulations, but also because it builds trust and protects the valuable data of customers,” Kumar concluded.
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