AI security best practices

LLM-SCM-Vulnerabilities-KrishnaG-CEO

LLM03:2025 — Navigating Supply Chain Vulnerabilities in Large Language Model (LLM) Applications

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As the adoption of Large Language Models (LLMs) accelerates across industries—from customer service to legal advisory, healthcare, and finance—supply chain integrity has emerged as a cornerstone for trustworthy, secure, and scalable AI deployment. Unlike traditional software development, the LLM supply chain encompasses training datasets, pre-trained models, fine-tuning techniques, and deployment infrastructures—all of which are susceptible to unique attack vectors.

Agentic-AI-IaC-KrishnaG-CEO

Agentic AI and Infrastructure as Code (IaC): Pioneering the Future of Autonomous Enterprise Technology

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Infrastructure as Code is a modern DevOps practice that codifies and manages IT infrastructure through version-controlled files. It enables consistent, repeatable, and scalable deployment of infrastructure resources.

PenTest-Anthropic-KrishnaG-CEO

Penetration Testing Anthropic: Securing the Future in an Era of Advanced Cybersecurity Threats

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**Penetration Testing Anthropic** combines traditional penetration testing methods with a more nuanced understanding of human behaviour, cognitive psychology, and artificial intelligence (AI). The term “anthropic” refers to anything that relates to human beings or human perspectives, and in this context, it highlights the critical role human elements play in both security and attack strategies.

While traditional penetration testing often focuses on exploiting technical vulnerabilities in systems, Penetration Testing Anthropic goes beyond these boundaries by considering how human behaviours—both of attackers and defenders—can influence the outcome of a cyberattack. This includes social engineering tactics, cognitive biases, organisational culture, decision-making processes, and the integration of AI and machine learning into attack and defence mechanisms.

This approach represents a shift from purely technical penetration testing to a more comprehensive model that accounts for the psychological, social, and technological aspects of cybersecurity.