Security | Threat Detection | Cyberattacks | DevSecOps | Compliance

The constant evolution of today's threat landscape has organizations counting on security controls to keep the bad actors out and safeguard their people, sensitive data, critical infrastructure, operations, and brand. However, even the most sophisticated security tools can present a risk when improperly configured. And unfortunately, even the best security teams can make mistakes.

Not every SIEM solution is built for modern security operations. While Splunk is widely used for log management, many teams face unpredictable pricing, complex tuning, and slow investigations as environments scale. New-Scale Fusion takes a different approach, It combines behavioral analytics, dynamic risk scoring, and coordinated AI agents to help teams detect risk earlier and move investigations forward faster. Here are six ways Exabeam improves outcomes compared to Splunk.

When Hugging Face's security team sat down to reconstruct what had torn through their production infrastructure in mid-July, they had more than 17,000 recorded attacker actions to sort through, spread across a swarm of short-lived sandboxes with decoy activity planted to slow them down. They did what any competent team would do in 2026 and reached for a frontier model to help triage the logs. However, the commercial APIs refused.

• Zenity secures ChatGPT Workspace Agents across their full lifecycle, from posture management at build time to detection and response at runtime. • AgentForger showed how a single link could forge an autonomous AI agent that inherits a real employee's identity and access, a risk legacy security tools can't see. • Zenity's AISPM catches the misconfigurations these attacks rely on, such as agents that auto-approve sensitive actions or connect to privileged systems.

A privacy policy that only works in English is not a global privacy policy. It is an English-language policy that a global company happens to be using. That distinction matters more than most teams realize. Enterprises now centralize contracts, HR files, healthcare records, and support conversations from regional offices around the world into a shared AI platform, often assuming that whatever detection and masking logic works for their English-language content will work everywhere else. It does not.

AI models are becoming essential to enterprise innovation, but the sensitive data that powers them is creating new security and privacy challenges. Even when raw training datasets remain inaccessible, attackers may still identify whether specific information was used to train a model through membership inference attacks.

This article was co-written by Zach Rice and Joe Leon, both at Aikido Security. tl;dr Some credentials are meant to be public, but secret scanners still flag them as generic secrets. We wrote suppression rules for the most common ones and reduced false positives by ~2%. These rules now ship by default in Betterleaks. Secrets scanners are built on regular expressions. Each pattern targets a specific credential type, like an AWS secret access key, a GitHub PAT, or a Stripe token.

Accelerating security solutions for small businesses‍ Tagore offers strategic services to small businesses. A partnership that can scale‍ Tagore prioritized finding a managed compliance partner with an established product, dedicated support team, and rapid release rate. Standing out from competitors‍ Tagore's partnership with Vanta enhances its strategic focus and deepens client value, creating differentiation in a competitive market. AI has completely changed how startups build.

An OpenAI agent broke out of its test sandbox and autonomously breached Hugging Face with no human direction, an incident both companies called unprecedented. CYJAX examines why this doesn't fit existing threat actor categories, maps it to the standard attack lifecycle, and outlines three additions CTI teams should make to their collection plans and PIRs to track autonomous offensive tooling before it hits their own network. On 16th July 2026, Hugging Face disclosed that it had been breached.

Bitsight's annual Ratings Algorithm Update (RAU) has been in effect as of July 16, 2026. In preparation, RAU 2026 Preview was made available in April 2026. As in the past, RAU 2026 is an effort to account for the continuous evolution of the threat landscape the Bitsight security ratings seek to quantify. This year's update is focused on modernizing the rating by improving how it is composed from various risk vectors (RVs). In particular, this entails the following.