Reverse-Engineering-6-Thinking-Hats-KrishnaG-CEO

Cracking the Code with Clarity: Applying the Six Thinking Hats to Reverse Engineering

Reverse engineering—a term synonymous with code disassembly, system deconstruction, and intellectual rigour—is often seen through a purely technical lens. However, the process also involves strategic thinking, risk assessment, creativity, and cross-functional collaboration. This is precisely where Edward de Bono’s Six Thinking Hats framework finds powerful synergy.

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Strategic Minds, Digital Crimes: A C-Suite Guide to Cyber Forensics with the Six Thinking Hats

In an era where data is more valuable than oil, cyberattacks have become a persistent and sophisticated threat. From ransomware to insider threats and nation-state espionage, breaches are no longer a matter of if but when. Cyber forensics—the science of investigating and analysing digital evidence—has emerged as a frontline response to these evolving threats. However, technical tools and processes alone are insufficient. For the C-Suite, strategic thinking and structured decision-making are imperative during a forensic investigation.
Enter Edward de Bono’s Six Thinking Hats—a simple yet powerful framework that encourages parallel thinking. When applied to cyber forensics, this model empowers executives to approach incidents holistically, ensuring thorough analysis, controlled response, and long-term business resilience. This blog post explores how C-Level executives can correlate the Six Thinking Hats with various stages of cyber forensic investigations to optimise ROI, mitigate risks, and safeguard reputation.
Cyber forensics entails the identification, preservation, analysis, and presentation of digital evidence post-incident. It includes deep technical tasks like disk imaging, log analysis, malware dissection, and threat attribution.

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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.

AI-Data-Poisoning-KrishnaG-CEO

LLM04: Data and Model Poisoning – A C-Suite Imperative for AI Risk Mitigation

At its core, data poisoning involves the deliberate manipulation of datasets used during the pre-training, fine-tuning, or embedding stages of an LLM’s lifecycle. The objective is often to introduce backdoors, degrade model performance, or inject bias—toxic, unethical, or otherwise damaging behaviour—into outputs.

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K02: Supply Chain Vulnerabilities – A Comprehensive Guide for Software Developers and Architects

The modern digital landscape is increasingly dependent on complex software supply chains, making them a prime target for cyber threats. Supply chain vulnerabilities in software development can have far-reaching consequences, from data breaches to full-scale operational disruptions. Software developers and architects must understand these risks to design resilient systems and mitigate potential threats proactively.
This blog post will provide a deep dive into supply chain vulnerabilities, covering their origins, real-world examples, risk mitigation strategies, and best practices for securing software ecosystems.