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LinkedIn Adds Message Safety Tools And Focused Inbox

The business networking and recruitment platform is cracking down on spam messages and scams.

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linkedin adds message safety tools and focused inbox

LinkedIn has just made some changes to its direct messaging service to help its 875 million members avoid spam and attempted scams. The business networking site is seeing growth rates of up to 34% a year, with 21 inMails sent every second, so it is keen to ensure that new and existing users are kept safe.

LinkedIn’s first change is the rollout of a new “focused” option for incoming messages, which will relegate unwanted mail to an “Other” box. Switchable automatic spam and harassment detection should help to send spam and legitimate mail to the right places, and a new feature is also available to report unwanted messages.

The company states that Focused Messages will help to present “the most relevant new opportunities and outreach”. LinkedIn says that the feature uses AI algorithms which learn from what you open and interact with to tailor a custom inbox experience.

Also Read: How To Find The Best Remote Work Opportunities In The Middle East

Alongside these messaging additions, LinkedIn says it will also add live captioning to video messages to improve accessibility — a move that is thought to signal the platform’s greater focus on messaging as a more standalone service.

The new features were being tried out in smaller control groups to gauge their effectiveness but are now going global, with all users expected to see the updates soon.

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ESET Bets On Specialized Security AI At 0.5% The Training Cost

ESET says NeoCortex can estimate an attacker’s next move, with a kill switch and human oversight built in to keep it from going rogue.

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eset bets on specialized security ai at 0.5% the training cost

A sign-in anomaly, a suspicious process launch and an unusual connection may look harmless alone. Together, they can describe an intrusion that is hard for security to teams to piece together.

To solve that problem, ESET announced ESET NeoCortex on November 4, 2026: a specialized foundational AI model for cybersecurity. It powers ESET LiveCortex, available within the ESET PROTECT Platform, which connects fragmented security signals and turns them into actionable incidents.

Filtering is the main selling point, with LiveCortex processing billions of raw telemetry events daily, and just 0.00111% of them contributing to incidents presented to customers. The aim is less alert fatigue and more analyst time spent on threats that matter.

NeoCortex is built on world-model architecture, learning how digital environments behave and how events relate. ESET says that lets it reconstruct missing incident context, recognize similar past cases and estimate an attacker’s likely next step – what the company calls “minus-one-day defense,” a chance to intervene before the next stage unfolds. ESET points to Black October, the week leading up to October 25, 2026, when critical security issues affecting PHP, Linux, GitLab and other key systems demanded rapid, prioritized responses, as the kind of moment where that context matters.

ESET’s internal research also suggests a specialized model like NeoCortex can be trained at up to 200 times lower cost than a large general reasoning model, while delivering more accurate, defense-focused results. The company credits optimized training, a focus on cybersecurity vocabulary, lower computing requirements and privately owned hardware.

Also Read: Dubai Says Its Deepfake Detector Needs Just 7 Seconds Of Video

“AI is accelerating both cyberattacks and cyberdefenses. With ESET NeoCortex, we are applying our AI investment to develop specialized foundational cybersecurity intelligence that remains under our control and strengthens, rather than replaces, human expertise,” said Juraj Malcho, Chief Technology Officer at ESET.

As part of the design, NeoCortex forms part of a layered architecture combining specialized models, deterministic controls, correlation logic, validation, auditability and expert human oversight. Safeguards include restricted sandboxes, read-only tools, continuous monitoring, defined usage and token limits, a kill switch and human oversight of critical response decisions. The model is also developed and self-hosted on ESET’s own infrastructure, which the company says reduces exposure to shifts among third-party AI providers, including commercial and geopolitical ones.

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