The digital landscape is entering a critical turning point, shaped by two game-changing technologies: generative AI (GenAI) and the imminent arrival of quantum computing. These technologies hold vast promise for innovation, but they also magnify the risks to privacy, data security, and trust. Organisations that want to thrive sustainably in this new era must adapt quickly, recognising that the traditional methods used to protect personal data will no longer suffice.Privacy has long been a legal obligation for organisations. Today, its much more than that. In fact, privacy has become a competitive differentiator organisations that handle customer data with integrity can build stronger relationships and earn more loyalty.Currently, around 75% of the global population is covered by modern privacy laws, which signals that privacy is increasingly seen as a universal right. However, despite these widespread legal frameworks, there are still significant gaps in how laws are executed across different regions and industries. Data breaches continue to escalate, misinformation is increasingly rampant, and consumers are becoming more sceptical about how their personal data is handled. The rise of GenAI has only intensified these challenges as machine-generated content blurs the lines between fact and fiction.Meanwhile, quantum computing looms on the horizon, introducing an entirely new set of challenges. By 2029, the computational power and availability of quantum systems is expected to make current encryption methods obsolete, putting sensitive data at unprecedented risk. For many organisations, the sheer cost of ensuring that this data remains secure could become unmanageable, potentially forcing them to purge vast quantities of personal data to prevent breaches.As the use of AI accelerates across industries, the quality of the data feeding these systems becomes even more crucial. However, too many organisations continue to focus primarily on protecting the confidentiality of data, while overlooking its integrity. This imbalance has led to a slew of problems, from poor decision-making to failed AI initiatives that fail to deliver meaningful outcomes.Gartner predicts that by 2028, organisations will invest as much in ensuring data integrity as they do in confidentiality. This is a major shift, and rightly so. For AI models to be effective, they need high-quality, trustworthy data to train on. If this data is flawed or unreliable, the resulting AI systems will be just as flawed and unreliable. Beyond AI, maintaining data integrity is critical for everything from regulatory compliance to safeguarding consumer trust in the organisations practices.In addition, data integrity plays a critical role in mitigating the risks posed by misinformation and AI-generated content. As GenAI continues to evolve, ensuring that data is accurate, traceable, and verifiable will become more important than ever. Without these measures, AI models risk becoming susceptible to manipulation, making them less effective and ultimately less trustworthy across industries.Read more on the intersection of AI and quantumQuantum computing development can benefit datacentres. Potential quantum computing uses include improving supply chains, financial modelling, and AI and machine learning optimisation.Microsoft unveiled Majorana 1, a quantum chip with eight qubits, aiming for a million. It focuses on scalability for breakthroughs in various fields despite current challenges.Middle East financial firms are investing heavily in quantum computing, with one of the worlds top quantum research centres in Abu Dhabi.The rise of quantum computing is not just a future concern; its a present reality that organisations must begin preparing for today. The concept of harvest now, decrypt later is already a reality, with malicious actors stockpiling encrypted data in anticipation of quantum breakthroughs that would render traditional encryption methods obsolete. This poses a grave risk to organisations, as sensitive information that is currently safe from hackers could one day be compromised by quantum systems.Governments around the world are already pushing for the development and adoption of post-quantum cryptography (PQC) encryption methods that are resistant to the computational power of quantum machines. But making the shift to PQC is no small feat. It requires a fundamental overhaul of existing cryptographic systems and infrastructure, a process that will take years to complete. For many organisations, the pressure is mounting to begin this transition as soon as possible to protect their sensitive data and remain ahead of the quantum curve.To navigate these challenges, organisations need to act decisively:Reassess Data Strategies: Move away from storing huge amounts of data to adopting data minimisation practices. Retaining only necessary information reduces risk and aligns with modern privacy regulations.Invest in Data Integrity: Apply robust measures to ensure data accuracy, provenance, and lineage. This is critical for AI applications and for maintaining consumer trust.Adopt Post-Quantum Cryptography: Begin developing crypto-agility and a migration to quantum-resistant encryption methods now to safeguard sensitive data before quantum computing becomes mainstream.Enhance Privacy Practices: Integrate privacy-by-design principles into every product and service, offering consumers granular control over their data.The intersection of GenAI and quantum computing represents a critical turning point for organisations. Failing to adapt to the evolving privacy and security landscape could lead to lost consumer trust, regulatory penalties, and competitive disadvantage. On the other hand, those who take proactive steps to protect data and embrace emerging technologies will not only minimise risks but also position themselves as leaders in the digital economy.Bart Willemsen is a VP analyst at Gartner, with a focus on privacy, ethics and digital society.