This presentation will show how AI is changing the face of cybersecurity for HPC and research parterships. Federated zero trust architectures using artificial intelligence give organizations the ability to implement a federated zero trust solution to ensure all distributed computing resources and data is protected while also providing for information sharing.
Conventional measures of security as by the perimeters of a network do not hold well today’s research environments characterized by interactions between multiple institutions, cloud based services, and HPC centers. Federated zero trust architectures using artificial intelligence provide a more flexible and robust approach.
In zero trust, all the traffic moving through the network is considered hostile, and users are permitted to access a resource for only a limited amount of time and only after verifying the user, the device that the user is using, and the context in which the user is operating.
AI Enables Dynamic, Risk-Based Authentication
Federated zero trust architectures using artificial intelligence leverage machine learning to dynamically assess risk and make intelligent authentication decisions. At runtime, the AI algorithms continuously study the user interactions and activities, device positioning, and network, along with threat intelligence to assess threats and risks. This allows precise user access control to go with the main concept of least privilege principle.
For example, AI-based authentication service might request extra identifications as soon as a researcher tries to reach the restricted section from an unfamiliar device/location. Federated zero trust using artificial intelligence therefore affords dynamic security that optimally protects while enabling user performance.
Federated Identity Streamlines Collaboration
Interdisciplinary studies are common nowadays to involve more organizations, and all of them have their own identity management systems. Managing the accounts manually across so many systems is complicated and is likely to involve many mistakes. Federated zero trust architectures using artificial intelligence solve this challenge by enabling federated single sign-on (SSO) via trusted identity providers.
In federated SSO, the details of the institutional credentials that the researchers have can be used to quickly access resources in other organizations. Intelligent this process up, makes use of artificial intelligence to map the users attributes and entitlements to detailed access policies. When AI is integrated into identifier management, it implements the same policy throughout the collaboration while also revoking access.
Securing HPC Workloads and Data
Unlike the typical enterprise application, scientific research and analysis requires High performance computing infrastructures that have special security necessities. HPC clusters are by design designed to be high-speed and thus the security aspect is often a compromise. Federated zero trust architectures using artificial intelligence can harden HPC environments without compromising usability.
Machine learning based microsegmentation implements workload isolation and least privilege access at the cluster level. Automated data governance combined with NLP and machine learning algorithms means that sensitive data for research can be made discoverable, secure and appropriately shareable across different federated enclaves. Hence federated zero trust using artificial intelligence delivers single-vendor security for diverse HPC and storage assets.
Continuous Risk Assessment and Response
That is why the primary tasks of cybersecurity are threat identification and immediate actions against them. AI thrives when it is applied to massive volumes of machine data that are created at HPC settings to identify concealed threats. The user and entity behavior analytics results performed by AI help to define the patterns of normal activity to determine insider threats or a compromised account.
Federated zero trust architectures using artificial intelligence integrate real-time risk assessment across all layers of the technology stack. Using AI automation enables organizations to respond to incidents proactively and correlate multiple alerts, coordinate containment measures, as well as update access policies in real-time. This is important since research workloads and threats change continually, and continuous learning and adaptation are critical.
Balancing Security, Usability and Performance
While federated zero trust architectures using artificial intelligence offer compelling security benefits, implementation requires carefully balancing protection, user experience and system performance. Some policies can be very prescriptive and limit the ability to innovate as well as slow down individual research.
The organizations should consider a stepped approach starting with federated zero trust leveraging artificial intelligence on the most sensitive data and servicesonal. Using AI trust scoring, high-risk application areas and overblocking that hinders research activity can be averted. Indeed, integrating LRs to AI means that over the years, AI will allow access decisions to become more smooth and natural and relatively unobtrusive to the user.
Towards an AI-Powered Zero Trust Future
The combination of zero trust technology stacks and artificial intelligence means federation can attain groundbreaking security for new research initiatives in areas such as pharmaceuticals, climatology, genetics, and more. Intelligent fine-grained access for the orchestration of distributed infrastructure empowers research organizations to advance innovations alongside the protection of their most precious assets.
There is constant investigation on how federated zero trust can work with AI, and its enhancements, functionality, scale, performance, and automation. For instance, new innovative privacy-preserving machine learning can help in secure and intelligent access control of highly sensitive data that crosses institutional settings. AI security is progressing at a very high rate, necessitating federated zero trust architectures to be a foundational component of global research collaborations and science.
