AI Quantum Resilience Strategies Emerge Amid Growing Security Concerns
Organisations consider security risks the leading barrier to effective AI adoption on their data. AI’s value depends on amassed data, but security risks exist in building and training models. These risks go beyond known threats to intellectual property during inference. Organisations must manage threats throughout their AI development and implementation processes.
Companies should prepare to change security protocols, as these changes will become mandatory if quantum computing-powered decryption tools become easily available. Training data can be manipulated, degrading outputs, and models can be extracted or copied. Sensitive data used during training or inference can also be exposed.
Quantum Threats to AI Data Security
Current public key cryptography will become vulnerable in the next ten years as capable quantum systems may emerge. Better organised groups are already collecting encrypted data to decrypt when quantum facilities become available. Any dataset with long-term sensitivity, including model training data, financial records, or intellectual property, requires protection against future decryption.
A migration to quantum-resistant cryptography will affect protocols, key management, system interoperability, and performance. This migration is likely to take several years. The risk from quantum computing’s ability to decrypt data is less immediate, but its implications should affect data and infrastructure decisions made today.
Introducing Crypto-Agility and Hardware-Based Trust
Utimaco advocates for ‘crypto-agility’, defined as changing cryptographic algorithms without redesigning underlying systems. ‘Crypto-agility’ is based on hybrid cryptography, combining established algorithms with post-quantum methods, such as those suggested by NIST. However, cryptography alone does not address all possible risks.
Utimaco also advocates for hardware-based trust devices that can isolate cryptographic keys and sensitive operations. If companies develop their own AI tools, protection should extend throughout the AI lifecycle. Hardware keys used to encrypt data and sign models can be generated and stored inside a boundary, verifying model integrity before deployment and protecting sensitive data.
Hardware Enclaves and Compliance in AI Security
Hardware-based enclaves isolate workloads, preventing even system administrators from accessing processed data. Hardware modules can verify a trusted state before releasing keys through external attestation, creating a ‘chain of trust’ from hardware to application. This approach establishes hardware-based trust mechanisms for high-value assets.
Hardware-based key management produces tamper-resistant logs for access and operations, supporting compliance frameworks like the EU AI Act. These measures contribute to a strengthening of controls throughout the AI development and deployment lifecycle and the introduction of ‘crypto-agility’ for a transition to post-quantum security.
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