Security | Threat Detection | Cyberattacks | DevSecOps | Compliance

Applications can change several times between scheduled security assessments. New features, APIs, and integrations may introduce risk long before the next annual penetration test begins. That gap is pushing offensive testing beyond a single tool or a single point-in-time engagement. Teams are increasingly combining Dynamic Application Security Testing (DAST), AI penetration testing, and AI red teaming to evaluate different layers of application risk.

The organizations that thrive in 2030 will be those that treat governance as the foundational architecture for innovations in autonomy.

On August 2, 2026, Productiv told customers its SaaS management platform was shutting down on August 6, with account data deleted once access ended. Four days is not much time to pull years of app inventory, spend, and usage data out of a system you've come to depend on, especially with AI tools now adding a fast-moving new layer of spend and access to track on top of everything else.

Thanks to smartphones, taking photos has become an important part of our lives to share moments with friends and family. Thanks to cloud storage services, we can store, share, back up, and sync our photos easily, but finding the best way to share photos online privately can be a bit more challenging.

By the time you realize what’s missing, it’s already gone. That’s the brutal reality of data exfiltration, the unauthorized transfer of sensitive data from a system or network to an external destination. And it’s happening more often than you think. It’s now the leading form of insider threat in the U.S., accounting for 62% of all such cases.

Enterprises are already spending an average of nearly US$2.5 million annually on logging solutions, and those tools consume 45% of observability budgets while teams juggle an average of seven different tools to manage logs and telemetry (enterprise logging spend and tool sprawl). That's the reason log management tools have moved from back-office utilities into security architecture decisions.

Financial institutions are not short on cybersecurity policies, frameworks, or regulatory requirements. Turning those requirements into a living risk management program can be challenging. Organizations need a program that can keep pace as technology environments expand, cloud adoption grows, third parties are added, and external exposures change. Especially as the threat landscape continues to evolve.

For the last several months, we have run AI agents against real production applications across industries. Two questions drove the work: what does AI-powered vulnerability analysis and pentesting surface at scale, and what does protection have to look like to keep pace. The vulnerability discovery side confirmed what Mythos made impossible to ignore.

The fever dream continues, and I'm not even in Vegas for Hacker Summer Camp. Last week I wrote about Anthropic disclosing that one of their models published live malware to PyPI while believing it was inside a simulation. I was running a fever when I read the report. The metaphor was too good: a model that couldn't tell simulation from reality, covered by a writer who wasn't sure which way was up. I thought that was a one-week story. Very naive of me to have so much faith, I know.

A leaked n8n API key is only the start. GitGuardian's research traces the full chain, from exposed tokens and weak keys to CVE-2026-25053 and the N8N_ENCRYPTION_KEY that protects every stored credential, then lays out a hardened configuration to break it.