Security | Threat Detection | Cyberattacks | DevSecOps | Compliance

‍Artificial intelligence (AI) systems and generative AI (GenAI) tools have already been embedded across enterprise operations in a myriad of ways that trigger compliance obligations, both in terms of AI-specific regulations and other reporting mandates. In many cases, this adoption is occurring informally, through employee-driven tools or AI features embedded within third-party platforms, without centralized visibility or approval.

As businesses continue to adopt new technologies and expand their digital ecosystem, about 72% of organizations report that overall security risks have never been higher. Access-related vulnerabilities, in particular, have emerged as one of the top cybersecurity concerns, since every new tool or system introduces additional access points, users, and permissions to manage.

MongoDB powers some of the world’s most modern applications.Everything from self-managed deployments to fully managed cloud environments run with MongoDB Atlas. But as teams scale across environments and projects, managing secure access becomes increasingly complex. Apono brings Just-in-Time, least-privilege access to MongoDB services across MongoDB, MongoDB Atlas, and MongoDB Atlas Portal.

For accounts that possess the utmost sensitive information and critical systems, managing who gets access to what is vital. This is where PAM tools play an important role. Privileged Access Management is a lucrative branch of IAM designed to monitor, record, and protect critical resources for accounts. However, have you thought about the repercussions of choosing the wrong PAM?

See how Torq harnesses AI in your SOC to detect, prioritize, and respond to threats faster. Request a Demo Your SOC is drowning. Industry estimates suggest that up to 60% of SOC analyst time is spent on Tier 1 triage, leaving less time for addressing real threats. According to Splunk’s State of Security 2025 report, 59% of security teams report being overwhelmed by too many alerts, and 55% waste precious hours chasing false positives.

This week, I am joining global policymakers and innovators in Davos for the World Economic Forum. The theme for 2026 is "A Spirit of Dialogue", a recognition that our toughest challenges require shared understanding and cooperation. As we gather to discuss the future of the global economy, we have an opportunity to lead an urgent conversation. It centers on the reality of artificial intelligence (AI), not the hype about what it might do, but on what it is already doing in our enterprises.

Security teams frequently investigate whether traffic is allowed, denied, or translated along a path, but traditional visualization methods create unnecessary complexity. A standard Path Search view includes every L2 and L3 hop—switches, routers, and intermediary devices that participate in forwarding but apply no security policy. While this depth is helpful for network troubleshooting, it overwhelms analysts focused specifically on enforcement points.

In the first round of the AI gold rush, most conversations about AI security centered on models: large language models, training data, hallucinations, and prompt safety. That focus made sense when AI was largely confined to generating text, images, or recommendations. But that era is already giving way to something far more consequential.

Remember when security was simple? Users had roles. Roles had permissions. Done. Those were the days when your biggest worry was whether someone from marketing accidentally got admin access to the finance system. Welcome to 2026, where that simplicity is dead.

On the surface, synthetic data looks like the safer option. It’s not real. It doesn’t point to an actual person. It can be reversed if needed. And it keeps systems running without exposing sensitive values. That logic makes sense. Until you look at how systems actually behave. Protecto supports both reversible synthetic data and tokenization. Referential integrity can be preserved either way. Mapping back is not the hard part. The difference is not whether you can recover the original value.