Security | Threat Detection | Cyberattacks | DevSecOps | Compliance

AI is not just reshaping cybersecurity. It is exposing where many organizations remain vulnerable. While attackers are racing ahead with AI-powered tools, too many defenders are still relying on outdated strategies, siloed data, and manual processes. In conversations with security leaders, I hear the same concern repeatedly. The anxiety is not just about AI-enhanced threats. It is about the growing sense that defenders are falling behind.

The explosion of generative artificial intelligence tools is sparking a wave of enthusiasm in workplaces, with employees eagerly embracing new applications to boost productivity and innovation. However, this adoption often leads to a new phenomenon known as shadow AI—the use of artificial intelligence tools within an organization without explicit approval or oversight from IT and security teams. Unsanctioned use of AI creates significant (and often invisible) security blind spots.

Research is what turns cybersecurity from a reactive scramble into a proactive discipline. It’s how security teams uncover new threats, pressure-test defenses, and understand the unintended consequences of innovation (especially as AI Agents reshape the attack surface).At Zenity, research isn’t a side effort. It’s how we build, challenge, and ultimately secure what’s next.

In the last year, generative AI has dramatically accelerated how software is written. Developers can generate entire functions with a prompt, automate repetitive logic, and offload everything from boilerplate code to documentation. But with this newfound speed comes a deeper, more complex challenge: ensuring that what’s being created is secure, trustworthy, and production-ready.

UNC6032 creates fake AI-themed websites, APT41 uses Google Calendar for command and control, and Void Blizzard targets critical sectors.

As data spreads across cloud platforms, SaaS apps, and remote environments, protecting sensitive information has never been more challenging. This is where Data Security Posture Management (DSPM) steps in.

Thanks to a wide range of use cases that automate manual activities, enterprises are rushing to integrate GenAI into their IT stack, only to realize they’ve hit a privacy wall. A concerning number of use cases involve the use of sensitive data like PII and PHI, risking data privacy and compliance. Enterprises today are becoming increasingly aware of these multifaceted risks associated with unfiltered AI usage and turning to the common solution available in the market – AI privacy tools.

Omdia, a global analyst and advisory leader, recently released a report called “How Extended Access Management (XAM) closes the gaps in security.” The report defines the challenges of modern access management as: These unmanaged forms of access create an Access-Trust Gap: The security risks posed by unfederated identities, unmanaged devices, applications, and AI-powered tools accessing company data without proper governance controls.