Parrot Security OS is a Debian-based GNU/Linux distribution specifically designed for penetration testing, digital forensics, reverse engineering, and cybersecurity audits . It is developed and maintained by the FrozenBox Dev Team , with a strong focus on security, privacy, and ethical hacking . Parrot Security OS is a powerful, flexible, and security-focused Linux distribution tailored […]

S.No. Use Case Description Example Tools/Technologies 1 AI-Driven Cyber Deception Creates decoy systems to mislead attackers Darktrace Antigena, Attivo Networks 2 5G and IoT Security Secures high-speed and distributed networks Cisco IBN, Fortinet IoT 3 AI-Driven SOC Automates threat detection and response in security operations centers IBM QRadar, Splunk UBA 4 AI-Powered EDR Detects and

AI-based tools are increasingly being used for network data analysis and cybersecurity , helping organizations detect threats, monitor traffic, identify anomalies, and automate responses. These tools leverage machine learning (ML), deep learning, and natural language processing (NLP) to enhance security posture and network performance. Here’s a list of popular AI-based tools used in the fields

Open source generative AI has unlocked incredible possibilities in research, education, and industry — but it also comes with significant challenges that must be addressed to ensure responsible, secure, and effective use. Below is an organized breakdown of the main challenges facing open source generative AI today: 🔍 1. Legal & Licensing Issues 📜 Problem:

Open Source Generative AI is rapidly transforming industries and enabling real-world applications across a wide range of domains. Unlike proprietary models, open-source generative AI allows for customization, transparency, scalability, and cost-effectiveness — making it ideal for startups, researchers, enterprises, and governments. 🌐 Real-World Applications of Open Source Generative AI Here’s a breakdown of how open

RAG Agents (Retrieval-Augmented Generation Agents) are a powerful fusion of information retrieval and generative AI , allowing models to answer questions or perform tasks using external knowledge sources instead of relying solely on their internal training data. 🧠 What Are RAG Agents? RAG Agents combine: This allows for: 🔍 Key Components of a RAG Agent

speech-driven models are AI systems that understand and generate spoken language , enabling more natural and accessible human-machine interaction. These models power voice assistants, real-time transcription, text-to-speech, speaker recognition, and even emotional tone detection. Speech-driven AI typically involves several types of models: 🧠 Types of Speech-Driven Models Type Purpose Examples Automatic Speech Recognition (ASR) Converts

These are a relatively new class of models that go beyond traditional language understanding or vision-language modeling by incorporating real-world action reasoning and execution , especially in agent-based systems . 🧠 What Are Language-Action Models (LAMs) ? LAMs combine language models (LLMs) with action execution capabilities , enabling them to: They represent a step toward

Vision-Language Models (VLMs) are a powerful class of multimodal AI systems that understand both images and text , allowing capabilities such as: 🧠 What Are Vision-Language Models (VLMs)? VLMs combine computer vision models (like CNNs or Vision Transformers) with language models (like Transformers or LLMs), learning to align visual features with semantic meaning in text.

“Open source generative AI” refers to generative artificial intelligence models and tools whose source code, training methodologies, and sometimes even trained weights are publicly available , allowing anyone to use, modify, and redistribute them. These models can generate text, images, audio, code, and other types of content. 🔍 What is Generative AI? Generative AI refers