Today’s AI Systems and need for High-speed interconnects Modern AI systems resemble supercomputers far more than traditional web-scale architectures. This is because AI workloads, especially distributed training using data-parallel, model-parallel, or tensor-parallel techniques, place enormous demands on east-west bandwidth between GPUs. These GPU-to-GPU exchanges include: Gradient sharing Parameter synchronization Checkpointing and state transfers Collective operations such
AI's Multiple Networks: Understanding the Layers of Connectivity Modern AI infrastructure uses multiple specialized networks, each with distinct performance, reliability, and cost requirements. These networks are typically organized into four or five tiers, ranked below from lowest to highest by speed, performance demands, and connectivity needs. Because each network tier has different requirements, they use different cabling and hardware at varying price points. Within each
The Critical Role of Networking in AI Beyond power and cooling, and beyond the specialized servers and AI accelerators themselves, one of the most essential, and often underestimated, components of any AI infrastructure is the network fabric that interconnects those systems. In NVIDIA’s AI Factory reference model, the network is not a supporting element; it is a core pillar that determines how efficiently AI workloads scale, communicate, and perform. Just as AI chips continue
Security CoPilot - Reference Diagram Overview The component labeled “Security Copilot” functions primarily as an orchestrator that bridges the Microsoft environment with the OpenAI LLM operating within Microsoft’s secure ecosystem. In Microsoft Security Copilot, Copilots function primarily as orchestrators rather than traditional AI models using embeddings or vector databases. Instead of performing semantic searches or querying internal data stores, Copilots leverage plugins
Standard IR Data Analysis phases A practical way to evaluate AI’s impact on incident response (IR) is by examining how it enhances the data analysis phases that analysts perform during alert triage and incident handling. These are analytical stages, distinct from the traditional IR lifecycle of Identification, Containment, Eradication, Recovery, and Lessons Learned. The core data analysis phases include: Planning, Search (including Data Collection and Parsing), Normalization,
Test Plan - Test 1: Installation and Configuration of Microsoft Security Copilot and Applicable Plugins Background Microsoft Security Copilot leverages AI-driven orchestration across Microsoft security tools using integrated plugins and agents. Its performance depends on Security Compute Units (SCUs), the measure of compute capacity required to run Copilot workloads. SCUs are billed per hourly activation, not per-minute increments. Each activation incurs a minimum charge of o
CRISC IT Systems Topic areas: Information Technology Principles Enterprise architecture: Managing and governing the overall structure of an organization's IT systems. IT operations management: Handling the day-to-day IT processes, such as change management, IT asset management, and incident management. Project management: Applying risk management principles throughout the system development life cycle (SDLC). Disaster recovery management (DRM): Creating and maintaining a plan
Moving from Risk Assessment to Risk response & Reporting Once a risk has been identified and assessed, the next step is to ask: What will we do about it? This is where risk treatment or risk response comes into play, selecting the most appropriate action to manage the risk. Risk Response Risk and Control Ownership: Assigning accountability for risks and the controls that address them. Risk Treatment/Response Options: Deciding on the appropriate strategy for addressing ident
Risk Evaluation & Risk Assessment Once the scope of the risk analysis is clearly defined, the next critical phase is Risk Assessment & Evaluation. This stage involves assessing the potential risks to the organization's people, assets, and data within the context of the defined system or environment. Risk Assessment & Evaluation serves as the analytical core of any cyber risk management process. While much of the industry content and discussion around cyber risk tends to focus
CRISQ Topic area overview Cyber risk management often feels complex due to the variety of frameworks, terminology, and implementation approaches in circulation. This article serves as a study aid for the Certified in Risk and Information Systems Control (CRISC) certification by breaking down key concepts within a simplified, practical risk management framework. At a high level, effective risk management can be distilled into four core functions: Risk Governance – Define the m
Security Copilot Product Testing These concepts are presented to establish a foundation for evaluating what Security Copilot can do today, the effort required to implement those capabilities, and how that may evolve with future enhancements. For instance, the transition from the Planning phase to Data Search may not yet be fully dynamic, particularly when the data required for analysis originates outside the Microsoft ecosystem. Microsoft has long incorporated guided response
