The future of a trusted and secure computing environment hinges on our collective ability to deliver solutions that improve the performance across a variety of workloads, while also optimizing security.
This week, at Cyber Week in Israel, I am joined by partners, customers, and cybersecurity industry and policy leaders from across the globe. Intel is committed to providing silicon-based security solutions that address the most pressing issues. There are three key themes at the conference, highlighting the challenges and opportunities facing our industry.
Emerging Workloads Deliver More Data to Analyze and Secure
Incoming data is increasingly difficult to effectively leverage without the computing power to process and learn from its growing volume and complexity. Machine learning (ML) algorithms, and other artificial intelligence (AI) applications and capabilities, have achieved remarkable results and are being extensively used in different domains. ML algorithms often require access to sensitive data, especially as the focus on data privacy increases around the world. Limiting access to the right data may limit the outcomes that can be achieved with the use of AI. In the case of blockchain, the security and privacy of data join transaction scalability as key technical considerations.
Intel technologies provide unique capabilities that can help improve the privacy, security and scalability for data-centric workloads like AI and blockchain. We are in a position to accelerate customer success by helping protect algorithms and data for AI applications as well as digital assets and smart contract execution for blockchain solutions.
At Cyber Week, we are focused on security for these two data-centric workloads: AI and blockchain. Technologies like Intel® Software Guard Extensions (Intel® SGX) enable the ecosystem to design solutions with improved security and privacy. What makes Intel SGX compelling is that it provides a hardware trusted execution environment (TEE), allowing better protections for data in-use, at-rest and in-transit. Also, built-in CPU instructions and platform enhancements provide cryptographic assertions for the code that is permitted to access the data. If the code is altered or tampered, then access is denied and the environment disabled.
Security for AI: Efforts Focus on Securing AI Data
We see security, in the context of AI, in two implementations. First, there is security for AI, where we focus on protecting data, algorithms and parameters. Second is AI for security, where we use AI for the detection of advanced exploits. The Advanced Platform Telemetry capability in our Intel® Threat Detection Technology is a step toward improving the outcomes of AI for security.
In security for AI, a couple of usages start integrating security to improve the outcomes that AI solutions can deliver. First is multiparty machine learning, where access to critical data and the integrity of algorithms are enabled by using homomorphic encryption and hardware-based trusted execution environments, like Intel SGX. Second is federated learning, for applications where one can’t move the data to a centralized location. In this usage, data owners at the edge work collaboratively to improve a shared prediction model.
At Cyber Week, we are highlighting several collaborations to add security to AI implementations.
- We are collaborating with Docker* to help make AI more secure, useful and shareable for federated learning, by hardening containers with Intel silicon-based security technologies.
- Intel researchers are making great strides toward practical methods for homomorphic encryption, a method that will allow computer systems to perform calculations on encrypted information without first decrypting. Duality* is collaborating with Intel to explore the security challenges of AI workloads using homomorphic encryption on Intel platforms. Duality* will use homomorphic encryption across every stage of an AI solution pipeline to minimize data exposure.
- Fortanix* announced enhancements to its Runtime Encryption solution to help enable secure execution of ML algorithms, using Intel SGX enclaves, with support for Python and R languages commonly used in research and modeling. This, in turn, supports secure data sharing and analysis for AI training models and applications.
Security for Blockchain: Industry Adoption and Collaboration
Blockchain continues to show promise in transforming business processes. Intel processor technologies offer capabilities to help improve the security, scalability and privacy of distributed ledger networks. At Cyber Week, we are introducing innovations in what we call “off-chain computing” to help address both privacy and throughput for blockchain implementations. We are also highlighting recently announced collaborations.
- Enigma* has developed a unique privacy protocol that uses Intel SGX to protect data, while allowing computation over the data. In our collaboration, we’ll work together to integrate this functionality for private smart contracts on the Ethereum public ledger.
- Two weeks ago, Intel joined SAP* to formalize efforts in a blockchain consortium to construct a blockchain proof of concept to improve international shipping efficiencies on SAP’s blockchain-as-a-service platform.
- Last month, the Tel Aviv Stock Exchange*, Accenture* and The Floor* announced the development of a new blockchain securities lending platform powered by Intel. This platform will transform the securities lending market in Israel by enabling direct lending among all the major financial instruments.
Security is pivotal to our company’s strategy and a fundamental underpinning for all workloads, especially those that are as data-centric as AI and blockchain. We will continue to innovate and make our silicon an active participant in the threat defense lifecycle. The announcements at Cyber Week underscore the value that our investments can deliver to meet the cybersecurity needs of organizations today.
Rick Echevarria is vice president in the Software and Services Group and general manager of the Platforms Security Division at Intel Corporation.