AI SAST is static application security testing enhanced with artificial intelligence to make code scanning more accurate, contextual, and actionable.

A software bill of materials (SBOM) is a machine-readable inventory of the components, libraries, modules, and dependencies inside a piece of software.

Penetration testing, or pen testing, is an authorized security assessment that uses simulated attacks to find and validate exploitable weaknesses in your systems. The goal is to understand how a compromise could happen, what access it would provide, and which defenses need improvement.

Effective penetration testing begins with defined business objectives, an agreed scope, and the appropriate testing model.

Exploitability measures how feasible it is to use a vulnerability. Contextual exploitability checks whether the necessary conditions exist in your deployment.

Tal Moriah

October 2, 2026

Endpoint detection and response tools compared: see the top EDR platforms, how to evaluate them, and where cloud detection and response fills the gaps.

Ziad Ghalleb

September 30, 2026

Application detection and response (ADR) is an application security approach that detects and stops attacks from inside running applications.

Tal Moriah

September 25, 2026

Runtime security tools compared: how Wiz, Sysdig, Falco, CrowdStrike, Datadog and more protect live cloud workloads, and which are worth a proof of concept.

Allison Jackson

September 25, 2026

Cloud FinOps is an operational framework and methodology that brings together stakeholders across finance, engineering, product, and the business to maximize the value of cloud & AI investments.

Looking for an Upwind alternative? Compare the leading platforms enterprise CISOs evaluate, including Wiz, Sysdig, Palo Alto Networks, and CrowdStrike

Learn how AWS penetration testing validates exploitable weaknesses in workloads, IAM, and configurations, then turns findings into fixes engineers can ship.

AI infrastructure is the hardware, software, and networking used to build, train, and run AI models. Learn its core components, benefits, and security.

# Meta Description Agentic AI use cases explained: how autonomous AI agents work across customer service, IT, finance, and cybersecurity, plus the new security risks to manage.

Agentic AI vs generative AI: generative AI creates content from prompts, while agentic AI plans and takes multi-step actions on its own. See the key differences.

The A2A protocol is an open standard that lets independent AI agents discover each other and coordinate tasks. Learn how it works and how to secure it.

AI code review uses AI to flag bugs and security flaws in pull requests before they merge. Learn how it works, its benefits, limits, and best practices.

Tal Moriah

September 8, 2026

AI detection and response (AIDR) is a security capability that monitors your AI systems, prompts, agents, models, and the data pipelines feeding them, then acts when something goes wrong.

Offensive security is a proactive way to test defenses by attacking your own systems the way a real adversary would.

AI data integration is the use of machine learning, natural language processing, and large language models to automatically connect, clean, map, and move data from many sources into a single, unified view.