In today’s digital landscape, organizations rely heavily on data to drive decision-making, optimize operations, and enhance customer experiences. While significant strides have been made in securing data at rest (stored information) and in transit (data moving across networks), a critical vulnerability remains when data is being actively processed. During this phase, data is typically decrypted, making it susceptible to breaches, cyber threats, and unauthorized access. This concern is amplified with the growing reliance on artificial intelligence, where vast amounts of sensitive data are constantly being processed and analyzed.
Confidential computing presents a game-changing solution to this challenge. It enables data to remain encrypted throughout its entire lifecycle, including when it is in use. This innovative approach is increasingly becoming a promising option for enterprises that handle sensitive information.
AI’s expanding role in business
AI is rapidly transforming business operations, from automating workflows to enhancing fraud detection and optimizing supply chains. Nearly half (49%) of technology leaders said in a PwC survey that AI was ‘fully integrated’ into their companies’ core business strategy. However, securing the data it processes becomes increasingly crucial as AI becomes more embedded in everyday business functions.
One of AI’s greatest strengths lies in its ability to analyze massive datasets and generate actionable insights. AI automates repetitive tasks like data cleaning, preprocessing, and report generation, saving time for data scientists. Financial institutions, for instance, may use AI to detect fraud and assess risks, making processes more efficient and reducing the need for manual data analysis.
The challenges of AI
However, if this data is not adequately secured, it remains vulnerable to breaches, potentially exposing proprietary business strategies and consumer information. It also means that any advantage acquired through the use of AI is at risk of being revealed to competitors.
AI-driven businesses across all sectors are realizing the importance of securing their data; however, taking action is more important for some organizations than others. Various sectors, including healthcare, finance, and government, regularly process critical data – such as patient records, financial transactions, and classified documents. A single data breach in any of these industries can have catastrophic consequences, ranging from reputational damage to financial losses.
Beyond cybersecurity threats, regulatory compliance is another major concern for businesses leveraging AI. Many governments have implemented stringent data protection laws, such as the General Data Protection Regulation (GDPR) in Europe and the California Consumer Privacy Act (CCPA). Non-compliance with these laws can result in hefty fines and loss of consumer trust.
The impact of confidential computing
Confidential computing provides the necessary safeguards to ensure AI models can process data without compromising security or privacy. By leveraging secure, isolated spaces within a processor, known as trusted execution environments (TEEs), confidential computing ensures that only authorized users can access sensitive data while keeping it shielded from external threats. Additionally, any hardware attacker that attempts to steal confidential computing environment data directly from memory only sees encrypted information.
Many organizations partner with third-party technology providers to deploy AI solutions, requiring them to share confidential data with external entities. This poses a significant security risk if the proper protections are not in place. Confidential computing mitigates this risk by allowing businesses to process data securely within AI models, ensuring that even external providers cannot access raw data. This provides companies with greater confidence in outsourcing AI operations without fearing unauthorized exposure.
Additionally, as the technology matures, businesses may explore shared confidential computing networks to optimize security while minimizing costs. Similar to the way financial institutions secure individual accounts within a larger banking system, confidential computing can enable multiple organizations to process data securely within a protected environment without compromising privacy. This collaborative approach can drive efficiency while maintaining data integrity.
For example, financial institutions will be able to collaborate on fraud prevention efforts by sharing details about suspicious transactions and accounts while protecting customer privacy. Using confidential computing, institutions can analyze this shared data securely and train AI models to identify fraud and money laundering schemes more effectively.
Challenges and adoption barriers
Despite its many advantages, adopting confidential computing comes with its own set of challenges. Implementing this technology requires an investment in new infrastructure and specialized expertise. Businesses must consider the cost of integrating secure enclaves and upgrading their systems to support confidential computing. Currently, large enterprises in data-sensitive industries are leading the way in adoption, while smaller businesses may hesitate due to budgetary constraints.
However, as confidential computing technology advances and adoption scales, costs are expected to decrease, making it more accessible to a broader range of organizations. The development of confidential computing-as-a-service models may further simplify adoption by offering flexible solutions that don’t require companies to overhaul their entire IT infrastructure.
The future of AI security with confidential computing
As AI continues to evolve, organizations must stay ahead of emerging security challenges. Confidential computing provides a future-proof solution that allows businesses to harness AI’s full potential while safeguarding critical data assets. Beyond individual company adoption, collaboration between cloud providers, AI developers, and cybersecurity experts will be essential in establishing standardized frameworks and best practices for secure AI processing.
By integrating confidential computing into their security strategies, organizations can build trust with customers, comply with regulatory requirements, and minimize cybersecurity risks associated with AI. As businesses navigate the complexities of the digital age, those that prioritize data protection through innovations like confidential computing will be better positioned to drive innovation while maintaining a strong stance on security.
Samuel Tourbot
Samuel Tourbot is Head of Cloud Communications at Alcatel-Lucent Enterprise. A seasoned cloud business growth leader with over 20 years of experience accelerating transformation and driving revenue through next-generation tech sales solutions, Samuel blends deep expertise in UCaaS, CPaaS, and digital transformation with a strategic, hands-on approach to cloud telephony and business planning—ensuring both customer success and robust bottom-line growth.
