Category: AI Governance & Risk
-
From Attack Trees to Threat Models
Turning Adversarial Paths into Defensible Architecture Attack trees are where good security conversations begin. Threat models are where they become actionable. Most organizations stop too early. They build attack trees: Then they fail to convert them into system-enforced guarantees. This blog explains how to turn attack trees into formal threat models that directly influence cloud,…
-
The Fifth Pillar of Cybersecurity: Ethical Awareness Beyond CIA and Zero Trust
1. Rethinking the Foundations of Cybersecurity For decades, cybersecurity revolved around three key principles, the CIA Triad: Later, Zero Trust Architecture (ZTA) refined this idea with its golden rule: “Never trust, always verify.” These principles built the modern internet’s security walls. But in today’s world of AI driven defense, automated incident response, and self-healing systems,…
-
AI Red Teaming: Breaking Your Models Before Attackers Do
How to stress-test, find, and fix the real vulnerabilities in your AI systems before someone else does. TL;DR AI red teaming is an adversarial, multidisciplinary practice that probes production and pre-production models to surface security, safety, privacy and misuse risks. It borrows from cyber red teams but expands to data, model artifacts, pre-trained components, prompt…
-
From DevSecOps to MLSecOps: Securing the AI Development Lifecycle
In recent years, organisations have matured their software-development practices through models like DevSecOps integrating security (“Sec”) into the development (Dev) + operations (Ops) lifecycle. Now, as artificial intelligence (AI) and machine-learning (ML) systems become core to business operations, a new discipline is emerging: MLSecOps (Machine Learning Security Operations). MLSecOps takes the DevSecOps ethos but extends…
-
Securing AI Plugins and Toolchains: Defense Beyond the Model
Introduction: The Model Isn’t the Only Attack Surface When we talk about securing generative AI, we often focus on the model itself its weights, its training data, its prompt vulnerabilities. But in modern systems the model is just one piece. Many solutions chain the model with plugins, APIs, orchestration layers, agent tools, and external services.…