Widespread use of AI presents a double-edged sword: an ability to increase innovation and efficiency while expanding the risk of data exposure and misuse. That blade has created a dilemma that undermines the promise of innovation and efficiency, and makes companies hesitant to fully embrace AI due to concerns around protecting sensitive data and brand value.

Today, DataKrypto is changing that, enabling secure AI-powered analysis of encrypted datasets and models. Based on our patented, high-speed fully homomorphic encryption (FHE) technology, our new FHEnom for AI™ solution safeguards both customized open-source AI models and proprietary models while ensuring the privacy and protection of sensitive data. The unique privacy-preserving AI solution defends against malicious activities, misuse, and adversarial manipulation, securing intellectual property and maintaining the confidentiality of critical datasets.

“The rapid acceleration of AI capabilities creates unprecedented risks for data privacy and security. As AI systems process ever-larger volumes of sensitive information, traditional data protection methods simply cannot keep up with the complexity and speed of these new technologies,” said Luigi Caramico, Founder and CTO of DataKrypto. “DataKrypto has responded to this urgent need with FHEnom for AI: ensuring that data privacy innovation matches the velocity of AI innovation and unlocks the technology’s true potential.”

AI security is a quickly evolving landscape with many players. This year’s RSA Conference, starting next week, will host more than 100 vendors with AI security solutions. The field is getting crowded! But, similar to other areas of cybersecurity, the majority of solutions focus on perimeter defenses, threat detection, and identity management, but not on protecting data.

“That’s where DataKrypto comes in,” said Caramico. “Our FHEnom for AI establishes trust and reliability in AI-generated outputs, helping enterprises gain confidence in AI to make accurate, safe, and fair decisions by protecting the data.”

AI Vulnerabilities: A Hindrance to Innovation

AI introduces significant data exposure risks, including the unintentional leakage of sensitive information by employees using both sanctioned and unsanctioned generative AI tools. Research shows that 77% of businesses have already experienced AI-related breaches. Herein lies the dilemma: AI’s remarkable progress is directly proportional to its ability to access and learn from vast, diverse datasets. The more data provided, the better AI’s outputs will be. But these datasets often contain sensitive personal information, whether it’s medical records, financial transactions, or biometric identifiers. The more AI systems learn, the greater the risk of inadvertently exposing private data or enabling misuse. As a result, many companies put strict limits on AI usage, slowing their ability to solve complex problems.

Data poisoning attacks also pose a serious threat by manipulating AI training data, leading to flawed models that can result in financial losses, harmful healthcare recommendations, security vulnerabilities, and the amplification of societal biases in critical applications.

There are several examples of how these risks impact companies, including:

Data Privacy Issues

• Regulatory Barriers and Compliance Complexity: Data protection laws like GDPR and CCPA are challenging for AI models to comply with due to the difficulty of removing embedded personal data, which can delay or limit AI innovation.
• Risk of Data Breaches and Loss of Trust: Data breaches in AI systems can result in significant financial and reputational damage, hindering AI adoption. 
• Operational Disruptions: Overly restrictive or misconfigured AI privacy controls can disrupt business and reduce productivity, slowing AI adoption.
• Ethical and Social Responsibility: Ignoring privacy concerns damages stakeholder trust and public acceptance, hindering innovation and market adoption.

Model Integrity Risks

• Vulnerability to Attacks: Data poisoning, adversarial attacks, and model manipulation can compromise AI systems, leading to incorrect or harmful outputs affecting critical operations, customer-facing applications, and more.
• Lack of Transparency and Explainability: Not understanding AI decision-making processes hinders organizations’ ability to validate outputs, ensure fairness, and meet regulatory requirements, stalling innovation.
• Resource Diversion for Risk Management: Addressing data privacy and model integrity issues requires significant investment in cybersecurity, risk management frameworks, and compliance processes, diverting resources from core innovation and slowing AI transformation.
• Potential for Operational and Reputational Damage: Model integrity failures cause operational, financial, and reputational damage, making leaders risk-averse and limiting AI initiatives.

Zero-Knowledge AI: Blazing a New Path to AI Innovation

FHEnom for AI is a practical solution allowing companies to address the critical problems of sensitive data leakage or model poisoning by malicious actors. It operates as a zero-knowledge AI framework that combines DataKrypto’s revolutionary FHE with Trusted Execution Environments (TEEs), constructing a comprehensive security framework to protect the integrity of AI models and maintain data privacy throughout the entire lifecycle, including training, inference, and deployment.

If an AI model is stolen, it will be useless, as it can only function within the TEE. The encryption key, which only exists within the TEE, protects data by ensuring that only users with the key can view the results generated by each query.

This dual-layer approach ensures AI providers cannot reconstruct raw user inputs/outputs, even during sensitive transformations. The TEE briefly manages plaintext operations within its secure memory space and then immediately re-encrypts the results, while FHEnom guarantees data remains encrypted during all operations.

Secure AI Benefits Business – and Humanity

It’s easy to imagine how this revolutionary new solution can have a significant impact on companies’ use of AI. For example, in healthcare and personalized medicine, patient data is securely anonymized and protected, which means AI can analyze vast health datasets to predict diseases, personalize treatment plans, and identify public health trends – improving outcomes and saving lives.

Within the financial services realm, institutions can deploy AI for fraud detection, risk assessment, and personalized banking when customer data is encrypted and access is tightly managed. Robust security and privacy frameworks foster trust, allowing banks to innovate with AI-driven services while protecting sensitive financial information from breaches or misuse.

AI model vendors can confidently deploy and monetize their proprietary and customized open-source models without fear of unauthorized access, model theft, or reverse engineering. They gain the ability to protect their intellectual property throughout the entire model lifecycle, ensuring that even when models are used in external or public-facing environments, their core assets remain secure. This protection empowers vendors to expand into new markets, offer models as services, and collaborate with partners, all while maintaining control over their innovations and preserving their competitive advantage.

When organizations proactively address privacy and security, they are better positioned to comply with evolving regulations (e.g., GDPR, CCPA). This not only avoids costly fines and reputational damage but also enables faster, broader adoption of AI innovations by reducing legal and ethical barriers.

AI use is growing rapidly. But for companies to truly benefit from its capabilities, AI innovation must be matched with AI security innovation. DataKrypto’s FHEnom for AI offers a zero-knowledge framework, safeguarding models and data throughout their lifecycle. This instills trust, enabling confident AI adoption across industries like healthcare and finance, fostering innovation and collaboration while ensuring robust data protection and regulatory compliance.

DataKrypto will exhibit at RSAC 2025, which will take place from April 28 to May 1, 2025, in San Francisco, at booth #ESE-32.

To schedule a meeting with a DataKrypto spokesperson, please visit https://datakrypto.com/rsac-2025/.