Real-world cloud security. Explained and applied.
A compromised Pod should not be able to “jump” from the cluster to the cloud. In practice, a bad combination of identities (IRSA/Workload Identity), excessive permissions, and lack of isolation can turn an app vulnerability into full tenant control. This article walks through how it happens, what signals to look for, and how to harden it without slowing operations.
A realistic kill chain (leaked credential → initial access → escalation → persistence) that ends in operational control of the cloud account in under an hour. Step by step, with concrete signals that would have allowed detecting it and how to harden the environment without playbook theory.
Break-glass accounts exist to regain control when everything fails. The problem: if they are created “just in case” and nobody governs them, they end up being the shortest path to internal abuse, lateral movement, and control evasion. This guide makes it practical what they are, how they are abused in real life, and how to operate them without turning them into a permanent backdoor.
SaaS integrations accelerate the business, but they also externalize part of your security perimeter to third parties. Forgotten tokens, excessive scopes, and “harmless” apps can become a path of persistent access to repositories, chats, and critical documentation. This article grounds the risk and how to audit real integrations in a company.
Terraform helps standardize, but it does not replace security controls. When a “quick” change is made in the console, drift appears: what runs in cloud stops being what the code declares. This breaks auditing, creates silent exposure, and is usually discovered late. Here I explain how it happens in enterprise, what signals give it away, and how to set up guardrails and continuous audit so control doesn’t depend on “waiting for the next apply”.
A minimal, operational playbook to respond in the cloud during the first 24 hours to an account compromise, secret leak, or privilege escalation. Signals, decisions, containment, validation, and a per-provider checklist.
Cloud segmentation fails less due to lack of technology and more due to dangerous combinations: overly permissive routes, uncontrolled peering, “temporary” rules that stick around, and a false sense of isolation between environments. This article reviews the real mistakes that open lateral doors to an attacker and how to verify them in practice.
What changes when an LLM operates a computer as an “agent” and how that fits into a Command & Control (C2) model. An operational walkthrough of the architecture, realistic abuse, detection signals, and controls that actually work in corporate environments.
Attackers are using “normal” cloud APIs and services (storage, messaging, repos, CDN, functions) as a Command & Control channel. This operations guide explains why it works in enterprises, what signals to look for, how to contain it without breaking the business, and how to set up verifiable guardrails in AWS/Azure/GCP.
Yes: AI can accelerate and scale attacks in Cloud, not “hack magically”. The real risk lies in automating reconnaissance, exploiting misconfigured identities, and abusing control planes, with autonomous loops that iterate until they achieve access or impact.