Terraform

Inside the Fortress: Why Perimeter Security Tools Fall Short and How Microsegmentation Protects Networks Internally

Companies invest heavily in perimeter defenses such as firewalls and intrusion detection systems, yet these measures no longer guarantee safety as attackers increasingly operate from within networks. Traditional L2 domains leave virtual machines unisolated, enabling traffic interception, lateral movement, and malware spread similar to an apartment building with poor soundproofing. Microsegmentation powered by SDN divides VLANs into isolated microsegments down to individual VM ports, enforcing granular policies based on ports, IP addresses, and protocols. This approach implements Zero Trust by placing virtual packet filters directly at VM network interfaces on the hypervisor, independent of guest OS actions. Performance remains high because filtering runs on powerful virtualization servers, and scaling occurs naturally as additional hypervisors absorb new workloads without extra configuration. A real-world case from the oil and gas sector shows one customer creating up to 5,000 new microsegmentation rules per week via open REST API. The technology complements rather than replaces perimeter firewalls, delivering both strict internal controls and operational agility.

Password Deleted from Git but Still Present: Major CI/CD Security Mistakes

Developers often leave secrets in configuration files, .env examples, or test scripts that end up in Git history. Removing a file in a new commit does not erase previous versions, and .gitignore offers no protection for already tracked content. Pre-commit hooks with tools like Gitleaks can scan staged changes before they reach the repository. Docker images can retain secrets in layers even after removal commands, requiring BuildKit secret mounts instead. Terraform plans and state files may still expose sensitive values marked only as sensitive. Kubernetes workloads need both non-root containers with dropped capabilities and narrowly scoped RBAC roles. Multiple scanning stages from pre-commit through post-deployment help catch issues before they reach production.

Unit 42 Details First Multi-Agent AI Ransomware Attack That Finished in Ten Hours

Palo Alto Networks Unit 42 has published the first confirmed case of a multi-agent AI ransomware operation. Attackers only defined the target; more than ten specialized AI agents then performed reconnaissance, credential harvesting, lateral movement, data exfiltration, and encryption within ten hours. The agents used over fifty ATT&CK techniques and successfully hid command traffic inside the victim’s own AI service endpoints. After encryption the same agents automatically generated an eighty-page security audit report listing every compromised system and technique. The sole defensive control that stopped part of the attack was a mandatory multi-person code review rule on Terraform changes. Unit 42 links the operation to frontier large-language-model frameworks and notes that earlier single-agent incidents such as JADEPUFFER have now evolved into coordinated agent fleets.