A practical guide to defining the protect surface and prioritizing Zero Trust for cellular networks, enabling an iterative rollout.

Explores islands of identity in agent-based IAM and why a single IAM falls short across SaaS and enterprise agents.

Explores discovery and traceability gaps in autonomous AI agents, real-time registries, and identity governance across cloud and on-prem environments.

Get the comprehensive CCSK v5 Prep Kit. Includes study guide, sample questions, and more to help you pass the cloud security exam.

Explores Zero Trust for agentic AI pipelines in cloud production, outlining identity, access controls, and guardrails to prevent machine-driven gaps.

Get step-by-step guidance to prepare your organization for the STAR for AI Level 2 designation. This resource explains upcoming updates to the STAR Registry, how to complete the AI-CAIQ, and what's ...

Written by Ken Huang, CEO & Chief AI Officer, DistributedApps.ai. This blog post presents MAESTRO (Multi-Agent Environment, Security, Threat, Risk, and Outcome), a novel threat modeling framework ...

When Agentic AI is integrated with NHI management, organizations gain a security model that’s adaptive, contextual, and built for modern systems. Risks are identified earlier. Response is faster. And ...

This Code of Practice shows how you can apply the CCM control set in your organization to reach STAR Level 2 third party certification/attestation and also remain ...

Explains RBI's .bank.in mandate, its aim to curb phishing and impersonation, and how banks sustain trust through DNS, certificates, and continuous compliance.

The AI Controls Matrix (AICM) is a first-of-its-kind vendor-agnostic framework for cloud-based AI systems. Organizations can use the AICM to develop, implement, and operate AI technologies in a secure ...

A Zero Trust implementation using Software-Defined Perimeter enables organizations to defend new variations of old attack methods that are constantly surfacing in existing network and infrastructure ...