Security | Threat Detection | Cyberattacks | DevSecOps | Compliance

Every year, CrowdStrike Professional Services performs hundreds of Technical Risk Assessments (TRAs) across myriad industries, geographies, and business environments. These deep, hands-on reviews look at how security controls behave in production to evaluate the threats they see and block — and crucially, the threats they miss.

Every DLP evaluation starts with the same frustration: The tools that dominated the market a decade ago were built for a threat landscape that no longer exists. Sensitive data now moves across SaaS platforms, AI tools, encrypted messaging apps, and personal cloud accounts, often in ways no file-level policy can follow. If you are evaluating DLP for the first time or replacing a tool that has underdelivered, this guide gives you the framework to ask the right questions and recognize the right answers.

If your login page no longer looks like the rest of your storefront, this isn’t a coincidence; Shopify's platform is changing. Shopify is officially deprecating Legacy Customer Accounts in 2026, with an aim to fully transition to the newer Customer Accounts framework. This shift is part of a broader move toward a more standardized and secure authentication model. While this improves consistency at a platform level, it changes how much control merchants have over the customer experience.

“Passwords are like underwear. You should change them often, and you shouldn’t share them with strangers.” This old adage, often attributed to information security expert Chris Pirillo, has been a cornerstone of cybersecurity advice for decades. As we approach World Password Day 2026, that advice feels more relevant than ever for IT professionals tasked with securing entire organizations.

May the 4th be with you. In celebration of Star Wars Day, here's what a galaxy far, far away can teach us about security. The films work surprisingly well as a case study, and not in the obvious way. It's not the lasers, androids or the lightsabers. It's that the Empire and the First Order both fall into the same trap most security programs walk into every day. In this post, we'll walk through what the films get right about modern security challenges, how AI is making them worse, and what to do about it.

Accelerating security solutions for small businesses‍ Tagore offers strategic services to small businesses. A partnership that can scale‍ Tagore prioritized finding a managed compliance partner with an established product, dedicated support team, and rapid release rate. Standing out from competitors‍ Tagore's partnership with Vanta enhances its strategic focus and deepens client value, creating differentiation in a competitive market.

AI governance is not a policy problem. It’s a visibility problem. Most enterprises are approaching it from the outside in: writing acceptable use policies, issuing guidelines, and hoping employees comply. That approach fails because it operates on assumption rather than evidence. You cannot enforce what you cannot see and most organizations have no reliable way to see what AI tools are actually running inside their environment.

Most enterprises are not running on a single cloud. The vast majority of organizations now operate in hybrid or multi-cloud environments and sensitive data follows wherever workloads go. Regulated files end up in S3 buckets. PII lands in BigQuery development tables. Source code copies into Azure Data Lake repositories that no policy anticipated. The problem is not that organizations chose to spread data across clouds. The problem is that most security programs were not built to track it.

Most current demonstrations of AI in security operations are lackluster. You ask a chat interface a question, get a summary, and maybe a suggested next step. The operator still does all the work, at human speed. Meanwhile, adversaries are already deploying AI offensively against their targets. AI in SecOps must ultimately be an operator. Otherwise, the gap between adversary and defender will become too wide to bridge. LimaCharlie Co-founder, Christopher Luft, demonstrates a simple way to get started.

In the latest episode of This Month in Datadog, Jeremy shares how to run autonomous Cloud SIEM investigations, remediate vulnerabilities with auto-generated fixes, and use natural language to explore Datadog. Later, Sumedha Mehta spotlights the Datadog MCP Server, which gives AI agents real-time access to Datadog’s observability data. Then, Chetan Sharma walks through Datadog Experiments, which measures how product changes impact the user journey.