Agentic-AI-Security-KrishnaG-CEO

Agentic AI Security Focus Areas: Strategic Guidance for C-Suite Executives and Penetration Testers

Agentic AI systems—autonomous artificial intelligence agents capable of reasoning, planning, and executing actions independently—are redefining digital transformation. These self-directed entities leverage multi-modal data, context awareness, and deep learning capabilities to perform tasks once reserved for humans. However, with increasing autonomy comes heightened responsibility. Ensuring these systems remain secure throughout their lifecycle is non-negotiable, especially for organisations operating in highly regulated sectors or those with sensitive customer data.
The Open Worldwide Application Security Project (OWASP) has provided a seminal guide to fortifying agentic AI systems. This blog offers a deep dive into the OWASP-recommended focus areas, bringing clarity to the security measures needed at every stage—from architectural design to post-deployment hardening. Targeted at C-suite executives and penetration testers, we translate technical depth into business-critical insights that focus on ROI, risk mitigation, and sustainable AI governance.

LLM-Integrity-KrishnaG-CEO

Secure System Configuration: Fortifying the Foundation of LLM Integrity

When deploying LLMs in enterprise environments, overlooking secure configuration practices can unintentionally expose sensitive backend logic, security parameters, or operational infrastructure. These misconfigurations—often subtle—can offer attackers or misinformed users unintended access to the LLM’s internal behaviour, leading to serious data leakage and system compromise.

LLM-Sensitive-Info-KrishnaG-CEO

OWASP Top 10 for LLM – LLM02:2025 Sensitive Information Disclosure

While theoretical risks highlight potential harm, real-world scenarios bring the dangers of LLM02:2025 into sharper focus. Below are three attack vectors illustrating how sensitive information disclosure unfolds in practical settings.

Agentic-AI-IaC-KrishnaG-CEO

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

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.