Blog

Sudo-Upgrade-Ubuntu-Linux-KrishnaG-CEO

Sudo in the Spotlight: Strategic, Secure, and Scalable Access Management

Upgrade Sudo demonstration in Ubuntu Linux 24.04 LTS from v1.9.15p5 to v1.9.17. CLI with video explanation even for beginners and Geeks.

Ubuntu-Linux-Kernel-Upgrade-KrishnaG-CEO

Upgrading Ubuntu Kernel v6.15.2

Upgrading Ubuntu Kernel v6.15.2 🔐 Linux Kernel v6.15 – What’s New & Security-Relevant 🗓️ Released: 26 May 2025 (With follow-up patches v6.15.1 and v6.15.2) 🗓️ Released: v16.15.2 on 10th June 2025 1. 🛡️ Security Enhancements 🔸 LSM (Linux Security Modules) Improvements 🔸 Spectre/Meltdown Updates 🔸 Kernel Address Space Layout Randomisation (KASLR) 2. 🔗 Networking Security …

Continue

EEAT-Agentic-AI-KrishnaG-CEO

EEAT Meets Agentic AI: Building Trust and Authority in the Age of Autonomous Intelligence

In an era defined by algorithmic decision-making, synthetic content, and digital hyper-efficiency, the emergence of Agentic Artificial Intelligence (AI) represents a monumental shift for businesses worldwide. Unlike traditional AI tools, which are reactive and rely on manual input, Agentic AI operates autonomously. These systems can pursue goals, make decisions, and adapt in real time—effectively becoming intelligent agents embedded within organisational workflows.

MetaSploit-PenTest-KrishnaG-CEO

The Ultimate Guide to Metasploit Alternatives for Penetration Testers

When it comes to offensive security and penetration testing, Metasploit Framework is a name that needs no introduction. As a powerful and widely adopted open-source platform, Metasploit continues to be a staple in the arsenal of security professionals. However, in recent years, several alternatives and competitors have emerged, offering varied capabilities in red teaming, post-exploitation, command and control (C2), and exploit development.

AI-RAG-Vulnerabilities-KrishnaG-CEO

LLM08:2025 – Vector and Embedding Weaknesses: A Hidden Threat to Retrieval-Augmented Generation (RAG) Systems

Retrieval-Augmented Generation is an advanced technique that augments pre-trained LLMs with external, domain-specific knowledge bases. Instead of relying solely on static training data, RAG-enabled models retrieve real-time contextual information, thereby enhancing relevance and accuracy.