Overcoming Data Silos, Intellectual Property Risks, and Regulatory Compliance with Confidential AI

The integration of AI in enterprise operations promises groundbreaking efficiencies and insights. However, harnessing the full potential of AI requires navigating a complex landscape of data sharing challenges, which Karthik Narain, Group Chief Executive, Technology, Accenture explored at our recent Confidential Computing Summit. 

From Karthik’s assessment, and from our own industry expertise at Opaque Systems, we know there are several obstacles enterprises must address to unlock the full value of sensitive data with confidential AI: data silos, concerns about intellectual property damage, exploitation of AI by bad actors, and growing privacy regulations.

A Solution to Power Sensitive Data Utilization

Sensitive data often resides in legacy systems protected by stringent controls and regulations, making it difficult to use in modern AI applications. Collaboration—internally among departments and externally with partners or service providers—is often hindered, if not downright impossible.

Model exposure also poses a new risk, potentially revealing training sets and insights into corporate strategies. Adversaries can exploit this by poisoning models or stealing them. Leaders are increasingly concerned about attackers using AI against their companies. Combined data sets can create unprecedented—and sometimes unauthorized—insights into consumer and business behavior, as demonstrated by a data search engine that exposed 26 billion records in the "Mother of All Data Breaches."

Meanwhile, regulations continue to emerge. Laws like DORA, GDPR, and the EU AI Act dictate how data must be collected, used, stored, and secured. Non-compliance can result in severe penalties, such as the fines imposed on Meta and Amazon for GDPR violations.

Despite these challenges, it’s apparent that businesses can’t afford to let data go to waste. AI simply holds too much opportunity. Yet most traditionally employed data obfuscation and anonymization techniques like sanitization, redaction and tokenization are cost-prohibitive, time-consuming, or don’t verify processes. 

Additionally, they can increasingly be reverse-engineered by adversarial AI. Confidential AI, meanwhile, overcomes most of the challenges at the intersection of data sharing and artificial intelligence.

The Promise of Confidential AI

Previously inaccessible data, trapped in silos, bound by regulations, or limited by corporate requirements, can now be put to work across industries, thanks to confidential AI. Confidential AI platforms, including the Confidential AI platform that our team at  Opaque Systems developed, ensure that AI models are trained and deployed within secure environments, preventing unauthorized access to training data and the models themselves. This means that sensitive datasets and proprietary algorithms are shielded from potential breaches.

By encrypting data and models during processing, confidential AI prevents exposure of training sets, patterns, IP, and insights into corporate strategies. Even if a breach occurs, the encrypted data remains inaccessible and unintelligible to attackers. Confidential AI platforms can even include mechanisms to verify the integrity of AI models. This prevents adversaries from tampering with or poisoning models to distort their outputs. Any attempts to alter the models would be detected and mitigated.

Ultimately, confidential AI enables secure collaboration between internal teams and external partners by sharing encrypted data. This ensures that proprietary information remains protected during joint AI development efforts.

Embracing the Future with Confidential AI

As enterprises navigate the complexities of data sharing and AI integration, confidential AI emerges as a transformative solution. It can unlock exciting opportunities to leverage sensitive data securely, and successfully solve the key challenges of data silos, intellectual property risks, and regulatory compliance. 

Businesses can now harness the full potential of confidential AI, driving innovation,  gaining a competitive edge, and moving AI projects into production faster, while safeguarding their most valuable assets. Embracing confidential AI is not just a strategic advantage; it is a necessity in the evolving digital landscape.

This article is inspired by presentations from Accenture at the Confidential Computing Summit. For more, view all sessions here.