Recorded Future has launched an AI Alert Filtering capability that uses machine learning to automatically prioritize security alerts by relevance, enabling analysts to focus on the most critical threats first. The solution maintains analyst control while automating initial alert triage to accelerate response workflows.
Read full article at Recorded Future ↗Interpol's Operation Jackal IV has resulted in 58 arrests and identified 263 cybercrime suspects distributed across 22 countries, representing a significant multinational enforcement action. The operation demonstrates coordinated international law enforcement efforts targeting cybercriminal networks on a global scale.
The U.S. Cybersecurity and Infrastructure Security Agency (CISA) has published the results of two red team assessments it conducted simultaneously against two critical infrastructure organizations, using what it described as similar tradecraft while recording sharply different defensive outcomes. Both organizations were fully compromised at the domain level, and in both, the red team also
Baby monitoring companies like Nanit are expanding their surveillance capabilities through AI-powered systems designed to track infants and young children's health, development, and behavior around the clock. These platforms are collecting increasingly granular biometric and behavioral data—from speech and language development to motor skills—while planning to extend their monitoring reach into early adolescence. The sector's significant funding and ambitions raise concerns about the scale and scope of data collection on minors who cannot consent to surveillance.
The SOC we've always known was built around a model that guarantees most of the alert queue will never receive analyst review. There's never time. In a traditional SOC, the typical progression follows a well-known pattern: an alert arrives; a detection engine assigns a severity score. The issue then waits for a human to decide if it should escalate to an investigation. Given the volume of