Security | Threat Detection | Cyberattacks | DevSecOps | Compliance

NetSuite testing becomes complex due to dependencies between customizations, multiple environments, and frequent updates. Effective testing requires clear scope, accurate environment selection, and version validation. Structuring tests around requirements, prioritization, and version history helps teams improve coverage, reduce inefficiencies, and ensure changes do not negatively impact existing functionality.

When weather forecasters predict hurricanes and blizzards, people rush to the grocery store for bread, milk, snacks, and water. While the snacks may be part of the storm preparation, the bread, milk, and water are part of the post-storm recovery. People know that they may experience power outages, water service disruption, or difficulty getting to stores. In short, the people plan how to recover in a disaster’s aftermath.

Software Composition Analysis (SCA) tools expose the risks in open source dependencies by identifying vulnerabilities, outdated dependencies, and license issues in your codebase. Top solutions include Mend.io (best for automated remediation and proactive SCA), Sonatype Lifecycle (known for enterprise policy management), Snyk (known for developer experience), and Checkmarx SCA (known for comprehensive coverage).

As work becomes more distributed and digital behaviors, both human and non-human, become more complex, organizations need more than activity data; they need context to understand risk, performance, and intent. Behavioral visibility analyzes workforce behavior patterns to detect engagement, sentiment, and insider risk signals. It helps security, IT, HR, and leadership identify emerging risks earlier, reduce false positives, and make more informed, proactive decisions in complex work environments.

When you are in crisis mode, you need less paperwork and more immediate action.

Generative AI–and large language models in particular–reached mass consumer adoption beginning in late 2022 and early 2023, with ChatGPT reaching 100 million users faster than any consumer application in history. Since then, AI has advanced at a breakneck pace and now seems to be incorporated in every tool, app, and website–regardless of how useful it might actually be.

Employees move sensitive data into AI tools every day. Someone pastes customer records into ChatGPT to draft an email. A developer feeds proprietary source code into a coding assistant to fix a bug. A project manager drops a confidential contract into Gemini to summarize it for a meeting. According to research from Cyberhaven Labs, 39.7% of the data employees share with AI tools is sensitive, and enterprise adoption of endpoint-based AI agents grew 276% in the past year alone.

Generative AI creates new attack surfaces that traditional security tools were not designed to address. The biggest generative AI security risks include prompt injection, data leakage, shadow AI, compliance exposure, model poisoning, insecure RAG pipelines, and broken access control. Each one requires a specific defense, not a generic firewall or DLP rule.

The most common first move in a modern cyberattack isn't a zero-day exploit or a piece of custom malware. It's a username and a password. Attackers know that credential theft is the fastest path into an enterprise network. And when an organization implements Single Sign-On (SSO), that path gets shorter. One stolen credential equals access to dozens of applications.

Managing JD Edwards user access by hand quickly becomes a hardship, especially as your team grows and roles shift constantly. You end up with deprovisioning that drags on for weeks, role assignments that don't match job descriptions, and audit trails scattered across emails and spreadsheets. These aren't one-off problems. They happen predictably when manual processes try to handle enterprise-scale demands in a fast-moving business environment.