Learn AI

AI Deep Dive

Category: security

  • Edge Computing Explained

    Edge Computing Explained for Novices (More Context) In the traditional model of cloud computing, data generated by devices and sensors is sent over a network to a centralized data center for processing and analysis. While this works well for many applications, it can face limitations when speed, reliability, and privacy are critical. Edge Computing offers… Read more

  • Nuclear Power for AI Infrastructure: Powering the Future

    Nuclear Power for AI Infrastructure: Powering the Future (More Context) Artificial Intelligence (AI) is rapidly transforming our world, powering everything from virtual assistants to complex scientific simulations. However, training and running these sophisticated AI models requires enormous amounts of computing power, which in turn demands significant energy consumption. As AI infrastructure scales, finding reliable, sustainable,… Read more

  • Decentralized Finance (DeFi): Banking Without Banks?

    Decentralized Finance (DeFi) Explained for Novices (More Context) For centuries, our financial system has been built on the foundation of centralized institutions like banks, stock exchanges, and payment processors. These entities act as intermediaries, controlling the flow of money and managing financial services. Decentralized Finance (DeFi) represents a radical new vision: a financial system built… Read more

  • Hybrid Computing: The Best of Both Worlds

    Hybrid Computing: The Best of Both Worlds (Even More Context) In our increasingly complex digital world, the demands placed on computing infrastructure are constantly evolving. From handling massive datasets for scientific research to powering real-time artificial intelligence applications, a one-size-fits-all approach to computing simply doesn’t cut it anymore. Hybrid Computing emerges as a strategic solution,… Read more

  • Ambient Invisible Intelligence Explained

    Ambient Invisible Intelligence Explained for Novices (More Context) Imagine a world where technology understands your needs and responds to them seamlessly, often without you even having to ask. This isn’t science fiction; it’s the vision behind Ambient Invisible Intelligence. It’s about weaving smart technology and artificial intelligence into the fabric of our everyday environments in… Read more

  • Post-Quantum Cryptography (PQC): Securing the Future

    Post-Quantum Cryptography (PQC) Explained for Novices (More Context) In our increasingly digital world, the security of our information relies heavily on cryptography, the art of writing and solving codes. Think of it as the invisible shield protecting everything from your online banking to government secrets. Currently, this shield is strong against regular computers, but the… Read more

  • Current Buzzwords in Tech (May, 2025)

    Current Buzzwords in Tech (May, 2025) A look at the trending terms in the technology landscape as of May 10, 2025. 1. Artificial Intelligence (AI) and its Subfields Generative AI (GenAI) AI’s ability to create new content like text, images, audio, and code, increasingly integrated into various applications. Details: Advancements in models, multimodal capabilities, ethical… Read more

  • Agentic AI Explained (Detailed)

    Agentic AI Explained for Novices (Detailed) Imagine a future where AI systems are not just tools waiting for your commands, but intelligent entities that can proactively understand your goals, plan their own actions, and work autonomously to achieve them. This is the vision of Agentic AI, a paradigm shift in artificial intelligence that moves beyond… Read more

  • BPM AI Agents Explained

    BPM AI Agents Explained for Novices (Detailed) Imagine the inner workings of a company as a network of interconnected pathways – these pathways represent the various business processes that drive operations, from fulfilling customer orders to managing supply chains and handling internal approvals. Business Process Management (BPM) is the discipline of understanding, designing, executing, documenting,… Read more

  • DynamoDB vs. MongoDB

    DynamoDB vs. MongoDB: Advantages of DynamoDB (Detailed) DynamoDB vs. MongoDB: A Detailed Comparison of Advantages for DynamoDB Both Amazon DynamoDB and MongoDB are prominent NoSQL databases known for their scalability and flexibility. However, their underlying architectures and feature sets lead to distinct advantages for DynamoDB in specific use cases. 1. Fully Managed and Serverless Architecture… Read more

  • Application architecture ideas to secure agentic AI applications

    Application Architecture Ideas to Secure Agentic AI Applications Here are some application architecture ideas specifically designed to enhance the security of agentic AI applications, building upon fundamental security principles. 1. The Guarded Agent Architecture Core Idea: Encapsulate each agent within a secure “guard” component that acts as an intermediary between the agent and the external… Read more

  • Python Libraries for Video Motion Detection – Real-Life Use Cases

    Python Libraries for Video Motion Detection – Real-Life Use Cases Python libraries for video motion detection are employed in a wide array of real-world applications, leveraging their capabilities for various purposes. Here are some prominent examples, categorized by the libraries often used: OpenCV (cv2) – Use Cases OpenCV’s efficiency and versatility make it suitable for… Read more

  • Data Structure of Trained ML Models

    Data Structure of Trained ML Models Once a machine learning model is trained, its “knowledge” is stored in a specific data structure that allows it to make predictions on new, unseen data. The exact structure varies depending on the type of model and the library used for training. However, the core idea is to save… Read more

  • A2A (Agent-to-Agent) vs. MCP (Model Context Protocol)

    A2A (Agent-to-Agent) vs. MCP (Model Context Protocol) A2A (Agent-to-Agent) vs. MCP (Model Context Protocol) Here’s a comparison between A2A (Agent-to-Agent Protocol) and MCP (Model Context Protocol) in the context of AI agents: A2A (Agent-to-Agent Protocol): Primary Focus: Standardizing communication and interoperability between different AI agents, regardless of their origin or framework. Aims to give AI… Read more

  • Model Context Protocol (MCP) Interfaces

    Model Context Protocol (MCP) Interfaces The acronym “MCP” in the context of interfaces most likely refers to the Model Context Protocol. This open protocol is designed to standardize how AI applications, especially Large Language Models (LLMs), can interact with external data sources and tools in a consistent and interoperable manner. What is the Model Context… Read more

  • How Banks Are Using Agentic AI and the Challenges

    How Banks Are Using Agentic AI and the Challenges Agentic AI, where AI systems act as autonomous agents capable of perceiving their environment, making decisions, and taking actions to achieve goals, is rapidly transforming various banking operations. Here’s how banks are leveraging this technology: Customer-Facing Applications: Personalized Financial Advice: AI agents analyze customer data, spending… Read more

  • How SAP and Oracle Can Use Agentic AI

    How SAP and Oracle Can Use Agentic AI SAP and Oracle, as leading enterprise software providers, are actively integrating Agentic AI capabilities into their platforms to enhance organizational productivity across various business functions. Here’s how they can leverage this transformative technology: SAP’s Use of Agentic AI: SAP is embedding “Business AI” across its portfolio, which… Read more

  • Non-Functional Requirements in AI/ML Applications

    Non-Functional Requirements in AI/ML Applications 1. Performance in AI/ML Model Accuracy/Performance Metrics Specify target metrics like precision (minimizing false positives), recall (minimizing false negatives), F1-score (harmonic mean of precision and recall), AUC (Area Under the ROC Curve for binary classification), RMSE (Root Mean Squared Error for regression), and acceptable error rates. Define how these metrics… Read more

  • Security Issues in LangChain and MCP Servers

    Security Issues in LangChain and MCP Servers Security Issues in LangChain Prompt Injection: Maliciously crafted prompts can manipulate the LLM to perform unintended actions, bypass filters, or disclose sensitive information. This is a primary concern as user input directly influences the LLM’s behavior. Example: A user might craft a prompt like “Ignore previous instructions and… Read more

  • Various MCP Servers and Cloud Availability

    Companies Developing MCP Servers and Cloud Availability A growing number of companies are actively developing and deploying MCP (Model Context Protocol) servers to integrate their services with AI agents. Many of these servers are designed to run in or interact with cloud environments. Companies with Developed MCP Servers (Examples) Technology Platforms Cloudflare: Provides infrastructure for… Read more

  • Exploring LangChain MCP Features with Sample Code

    Exploring LangChain MCP Features with Sample Code LangChain provides integration with the Model Context Protocol (MCP), allowing LLM agents to interact with external tools and data sources managed by an MCP server. This enables powerful capabilities like real-time information retrieval and action execution. Here’s an exploration of key LangChain MCP features with illustrative Python code… Read more

  • Use Cases: Enhancing Customer Experience and Business Operations with Data Science

    Enhancing Customer Experience and Business Operations with Data Science Enhancing Customer Experience and Business Operations with Data Science Data science provides powerful tools to understand customers better, personalize their experiences, and optimize core business operations. This article explores ten key use cases in these areas. 1. Customer Churn Prediction Domain: Customer Relationship Management (CRM), Telecommunications,… Read more

  • Microsoft Azure Business Intelligence (BI) Offerings and Use Cases

    Microsoft Azure Business Intelligence (BI) Offerings and Use Cases I. Data Warehousing Azure‘s primary data warehousing solution is Azure Synapse Analytics, a limitless analytics service that brings together data integration, enterprise data warehousing, and big data analytics. Key Features: Massively Parallel Processing (MPP): Designed for high-performance analytics. Columnar Storage: Optimized for query performance and data… Read more

  • DevSecOps: Integrating Security into the Entire SDLC

    DevSecOps: Integrating Security into the SDLC DevSecOps represents a fundamental shift in how security is approached in software development. Instead of treating security as a separate phase, it advocates for integrating security practices and considerations into every stage of the Software Development Lifecycle (SDLC), from planning to operations. The Core Principles of DevSecOps Security as… Read more

  • Detailed Explanation: Vector Embedding vs Feature Store

    Detailed Explanation: Vector Embedding vs Feature Store Vector Embeddings: Deep Dive Detailed Explanation: At its core, a vector embedding is a way to represent complex data as a point in a multi-dimensional space. The magic lies in how these representations are learned or constructed. The goal is to capture the underlying semantic meaning, relationships, and… Read more

  • Top Salesforce Concepts: A Detailed Discussion

    Top 50 Salesforce Concepts: A Detailed Discussion Salesforce is a vast platform with numerous features and functionalities. Understanding its core concepts is crucial for anyone working with it, whether as an administrator, developer, or end-user. Here’s a detailed discussion of 20 top Salesforce concepts: 1. Organization (Org) Your Salesforce instance. It’s a single, secure, and… Read more

  • Evaluating Salesforce and Mulesoft Anypoint Platform Integration

    Evaluating Salesforce and Mulesoft Anypoint Platform Integration Salesforce, a leading cloud-based CRM platform, offers a wide array of features for sales, service, marketing, and more. The Mulesoft Anypoint Platform is a powerful integration platform as a service (iPaaS) that enables connectivity across various systems, applications, and data sources. Integrating these two powerful platforms unlocks significant… Read more

  • Integrating Salesforce with Mulesoft: Events, Microservices, and APIs

    Salesforce Integration with Mulesoft: Events, Microservices, and APIs Mulesoft, a leading integration platform, plays a crucial role in connecting Salesforce with the external world. It acts as a middleware layer, facilitating communication and data transformation between disparate systems. Mulesoft can leverage Events, Microservices, and APIs to achieve robust and scalable Salesforce integrations. Let’s explore each… Read more

  • Salesforce Integration with the External World: Events, Microservices, and APIs

    Salesforce Integration with the External World: Events, Microservices, and APIs Salesforce, while a powerful platform on its own, often needs to interact with external systems to create a unified and comprehensive business solution. This integration can be achieved through various methods, with Events, Microservices, and APIs being prominent approaches. Let’s explore each of these in… Read more

  • SOQL: Salesforce Object Query Language – In Absolute Detail

    SOQL: Salesforce Object Query Language – In Absolute Detail SOQL (Salesforce Object Query Language) is a powerful language specifically designed to query data stored in the Salesforce database. It’s syntactically similar to standard SQL (Structured Query Language) but is tailored for the unique architecture and data model of Salesforce. Understanding SOQL is fundamental for any… Read more