Critical Infrastructure and AI Use Case
- A security breach will be experienced by 30% of critical infrastructure organizations. This type of action could lead to the shutdown of operations or cyber systems.
- Assailants will have weaponized a critical infrastructure cyber-physical system with the primary goal of causing harm or death to humans.
Critical infrastructures are now more connected than ever before, with staff able to access them via remote desktop protocols and VPNs. As a result, they are becoming a more visible and especially appealing target for institutional hackers and cybercriminal gangs alike. These scenarios add up to a Gartner survey conducted in the first half of 2021. According to the findings, 38% expect to increase their spending on operational technology security (OT). Given that OT networks are a high-risk target for future breaches, spending more money on security isn't out of the question.
The Current Status
The hacker initiates the attack by installing malware that targets the SCADA (supervisory control and data acquisition) systems of various utility companies. These businesses include energy and power plants, water and wastewater systems, and many more. Following these actions, these systems are weakened to the point where they can be severely damaged. An attack on a power station, for example, could cause a blackout.
The Problem
The primary goal of AI for critical infrastructures is to maximize efficiency, eliminate errors, and reduce risks to the greatest extent possible. Furthermore, innovations powered by AI, edge computing, 5G, IoT, and quantum computing will provide enterprises and nations with a competitive advantage. This also applies to critical infrastructure. These innovations will transform lives and lead to massive economic growth with the deployment of fully integrated Cyber-Physical Systems (CPS) for critical infrastructure. However, as more scalable and automated systems are deployed, the attack surface for expands, resulting in new threats.
The Solution
HUB Security provides a comprehensive confidential computing platform designed to secure AI-driven applications across critical infrastructures, allowing for faster and safer workflow. The platform can securely connect and run apps and data across critical infrastructure, making it future-proof against quantum and AI-based cyberattacks. The solution can also be provided as a managed service. HUB uses a new security paradigm centered on confidential computing to create a secure enclave for AI models and data, giving critical infrastructures working with machine learning and AI a competitive advantage. This approach enables multi-party analytics and collaboration by providing secure, isolated environments to protect the integrity and privacy of AI models and data.
