Security | Threat Detection | Cyberattacks | DevSecOps | Compliance

Turning a photograph into moving footage has become familiar. The details that make people keep watching usually come from the relationships inside the frame: why a character turns at that moment, why the product appears there, why the music lands on that beat, and how the camera carries the viewer toward what comes next.

An AI feature can reach production before anyone has decided who owns its credentials. A developer creates an API key for a prototype, a colleague copies it into a background worker, and a troubleshooting session puts the same value into a support ticket. The application works, but the team can no longer say exactly where its access begins or ends.

Ninety-five percent of organizations believe they have visibility into their AI and machine identity exposures, yet only 36% are actually monitoring them.

Software teams today are shipping code faster than ever before. New features go live weekly, sometimes daily, and the pressure to stay ahead of competitors means security can no longer be treated as a final checkpoint before release. This shift has pushed DevSecOps teams to rethink how they test for vulnerabilities.

Specialized team of AI agents that discover, attack, and validate web vulnerabilities, leveraging pre-existing site context to eliminate noise and speed remediation.

For years, security operations centres have operated under the same constraints of more alerts than analysts, more investigations than hours in the day, and more pressure than most teams can sustainably absorb. That imbalance is becoming dangerous as frontier models rapidly improve at finding vulnerabilities and turning them into exploits, while defenders are left dealing with the consequences in real time.

The company is building a new Exploit Path Verification (EPV) capability that will tell security teams which vulnerabilities an attacker can reach in their environment, turning long exposure lists into evidence-backed priorities.

Phone scams have been around for decades, but the last few years have brought a sharp shift in how convincing they've become. Voice phishing, often called vishing, used to rely on generic scripts and a scammer's ability to sound believable. Today, the same technology that powers helpful tools like AI phone calling is also being studied and used to fight back against these scams.

AI video gets much harder the moment you ask it to tell a story. A single beautiful shot can hide a lot of weaknesses. Narrative work exposes them: the character has to remain recognizable, actions need to happen in the right order, camera changes have to make sense, and the next clip should feel as if it belongs to the same world. That is why we are not ranking these tools by the prettiest demo. We care about how useful they are for building scenes, maintaining visual direction, working from references and turning multiple clips into something that feels intentional.

The most difficult AI incidents do not necessarily begin with an obviously malicious prompt. An employee can ask an approved agent to summarize customer data. The agent retrieves the correct records, invokes an approved tool, generates the requested output, and then sends it somewhere it should never have gone. Every individual action may look legitimate in isolation. The incident only becomes visible when security can reconstruct the entire sequence and understand what the user intended, what the agent inferred, which systems it touched, and where the execution path diverged.

It's no secret that AI has a significant presence in our daily lives these days. Many people hail it as a great way to save time and help them with basic tasks each day. The problem is, AI has become a part of nearly every single app, product, and device. AI has overreached the limits that the companies selling it promised. It's time for everyone to take action to ensure that AI products and companies are kept under control before they become problematic.

The smartest AI model in the room may still be running on someone else's computer. Teams focus on model quality and speed. They compare GPU availability and price. Control of the underlying infrastructure often receives less attention. That gap matters as AI moves into sensitive business systems. Models may process customer records. They may use intellectual property or regulated data. Some connect directly to production operations.

Shadow agents are quickly becoming one of the hardest visibility problems in enterprise security.

Less than three months after its commercial launch, OpenMatter Network today announced a significant expansion of the platform with new capabilities that make it easier for enterprises, developers and researchers to build, deploy and collaborate using sensitive data and AI while maintaining cryptographic control over how information is accessed, computed and shared.

A security analyst prepares a phishing report for publication. The original screenshot is accurate but messy: a personal email address is visible, the browser contains unrelated tabs, and a notification blocks part of the page. The analyst uses an image editor to clean it up. The result looks professional. Unfortunately, one character in the suspicious domain has also changed. Tools such as Nano Banana can edit existing images and generate new ones, but security communication introduces a requirement ordinary visual content may not have: some pixels represent facts. Improving the image cannot be allowed to rewrite them.

Bright Security Expands AI SDLC Security Platform, Launches AI PT: AI-Powered Penetration Testing That Cuts Weeks Down to Hours.

Twelve months of production telemetry reveal the scale, causes and security impact of configuration drift, pointing to firewall and EDR tool risk.