AI's role in Data Privacy: Navigating opportunities and risks

The landscape of Artificial Intelligence (AI) has seen a transformative shift with the introduction of Generative AI tools like ChatGPT in 2022, making AI accessible to a broader audience. This development triggered a reassessment of data usage in various industries, urging a closer examination of risks and measures to safeguard customer data and privacy. 

In the pre-Covid era, the integration of AI and cybersecurity, particularly in intrusion detection and non-signature-based inconsistency, was evident. However, the post-pandemic environment has witnessed a significant surge in dependency on AI for cybersecurity and resilience. 

As the world observed Data Privacy Day recently, it's crucial to delve into the evolving intersection of AI and personal information. Here we investigate AI's role in shaping data privacy, highlight potential threats, and scrutinise ethical considerations and regulatory safeguards protecting our digital autonomy. 

Experts foresee a growing impact of AI on cybersecurity, presenting both opportunities and risks. While cybercriminals can exploit AI to automate attacks and evade detection, these same tools empower cybersecurity professionals to counteract threats through innovative means. 
 
Data Breaches and Exploitation 

Unchecked AI algorithms can become breeding grounds for cyber threats, allowing unauthorised access to sensitive data. Despite digital advancements in 2023, data breaches remained a global concern. The Data Security Council of India reported 400 million cyber threats, averaging 761 detections per minute.  

Another report by PwC indicated that 38% of Indian companies felt highly exposed to cyber threats, leading to increased investments in cybersecurity with a focus on AI and machine learning. 

Samir Kumar Mishra, Director, Security Business, Cisco India & SAARC, emphasized the importance of privacy in AI usage. “AI requires massive amount of data. And when using data, respecting privacy is critical. Privacy risks in most business AI use cases are manageable with a thoughtful approach to governance,” said Mishra adding that we are seeing more organisations putting in place AI governance teams and frameworks to manage those risks. 
Businesses can build on a foundation of security and privacy and augment existing controls and processes to address the new risks and opportunities presented by AI. Privacy risks are also a concern for gen AI, he further added.
 
Biased Decision-Making 

AI models trained on biased data can exacerbate societal biases, posing threats to personal data protection. Opaque AI systems, AI bias, and AI hallucinations are concerns, particularly when AI lacks transparency. To address privacy issues, interpretable machine learning techniques and transparency standards for AI are being developed. 

Satyajith Mundakkal, CTO- Senior Vice President at Hexaware Technologies, stressed the need for a "privacy by design" approach in integrating AI. “The risk of AI perpetuating biases in the training data, potentially leading to privacy violations that unfairly impact specific groups,” said the CTO.

He emphasised that as such, ‘privacy by design’ becomes a critical approach when integrating AI, ensuring privacy considerations are woven into the development process from the outset. 

“Privacy professionals and AI developers must work in tandem to address these dualities,” Satyajith suggested.  
 
Deepfake Menace 

Deepfake technology, driven by advanced AI, raises concerns about spreading misinformation and manipulating identities. Using complex algorithms, deepfakes create realistic fake videos and images, blurring the lines between reality and fiction. This poses risks to personal privacy, as deepfakes can easily spread false information. 

Sundar Balasubramanian, Managing Director at Check Point Software Technologies, India & SAARC, emphasized the importance of proactive strategies to preserve privacy in the AI landscape. He stated, "Lack of information helps cybercriminals, who more recently are using tactics such as deepfakes to obtain sensitive information through deepfake voice calls or more commonly, through fake videos. To counter the potential invasiveness of AI, businesses must proactively implement strategies ensuring the preservation of privacy.”

In Balasubramanian’s opinion, effective mitigation of AI privacy risks demands a holistic approach, combining technical solutions, ethical guidelines, and robust data governance policies.  
 
Ethical AI Development 

Embracing ethical principles in AI, such as fairness and transparency, is crucial for responsible development. AI's role in marketing raises ethical concerns, including data privacy, fairness, and transparency. Global regulations like the EU's GDPR and India's proposed PDPB shape ethical AI and data privacy strategies, emphasizing individual rights and consent. 

Purshottam Purswani, CTO, Atos APAC, highlighted how these regulations define ethical AI and data privacy strategies, he said, “Internationally, regulations like the EU's GDPR and India's proposed PDPB define ethical AI and data privacy strategies by enshrining individual rights, limiting data use, and demanding consent, thus shaping a future where ethical AI protects personal data integrity.”  

Nader Henein, VP Analyst, Gartner, emphasized that AI trust and safety are becoming best practices. "As of today, AI trust and safety is a best practice, over time, I expect regulators and organisations to mandate standards and potentially certifications when investing in AI-driven products,” he said adding that these standards will govern the intersection of privacy and AI, with all the decisions driven by our personal data. 

In the evolving landscape of AI, balancing opportunities with vigilance is crucial to ensuring a future where technology enhances lives while safeguarding privacy.