In a revealing interview with Click Here, the leader of the LockBit ransomware gang, known as LockbitSupp, denies being Dmitry Yuryevich Khoroshev, the Russian national accused by US and British authorities of being the group's mastermind. Despite international law enforcement's identification of Khoroshev as LockbitSupp, complete with viral photographs and charges spanning 26 criminal counts with penalties up to 185 years in prison, LockbitSupp maintains his innocence, challenging the lack of concrete evidence against him. He asserts that the real LockBit group continues its operations unabated, planning future attacks even after a significant crackdown by authorities in February that saw the seizure of the gang's servers. Throughout the interview, LockbitSupp questions the basis for his alleged identification as Khoroshev, criticizes law enforcement tactics, and underscores his ambition to target 1 million companies, showing defiance in the face of escalating legal and operational pressures.

In interview, LockbitSupp says authorities outed the wrong guy The Record

The U.S. government agencies, including the FBI, CISA, and HHS, have issued a warning about the Black Basta ransomware gang, which is actively targeting the healthcare sector along with 12 other critical infrastructure sectors. This gang, responsible for attacking over 500 organizations globally since April 2022, infiltrates networks via phishing and exploits vulnerabilities, delaying ransom demands to complicate response efforts. Black Basta's tactics have severely disrupted operations for healthcare providers, such as the Ascension healthcare system, emphasizing the need for enhanced cybersecurity measures within the industry. The group, potentially linked to or a rebranding of previous Russian-speaking cyber threat entities, has already caused significant financial losses and operational challenges, underlining its threat to critical infrastructure and the importance of vigilance and prepared measures to combat such cyber threats.

After Ascension ransomware attack, feds issue alert on Black Basta group The Record

The Biden administration plans to enforce minimum cybersecurity standards for hospitals following a significant hack at Change Healthcare, affecting 100 million Americans by exposing patient data and disrupting financial operations. This cybersecurity push, led by Anne Neuberger, deputy national security adviser, responds to vulnerabilities highlighted by the hack, including delayed patient care and financial instability for clinics, with the breach potentially slashing UnitedHealth's profits by up to $1.6 billion. Alongside regulatory measures, the administration aims to offer free cybersecurity training to 1,400 small and rural hospitals. This initiative comes as the healthcare sector increasingly falls prey to cyber-attacks, evidenced by a recent cybersecurity incident at Ascension, a leading hospital network. Resistance from industry representatives like the American Hospital Association is anticipated, despite the clear need for improved security measures highlighted by these incidents.

White House Will Require Cyber Standards for Hospitals GovTech

Investigators at Cedars-Sinai's Smidt Heart Institute in Los Angeles developed an artificial intelligence (AI) method that effectively detects atrial fibrillation, a condition characterized by abnormal heart rhythms, from echocardiogram imaging. The deep learning model was trained with over 100,000 echocardiogram videos to distinguish between normal heart rhythms and those indicating atrial fibrillation. Remarkably, this AI can identify patients likely to develop or who have had atrial fibrillation within 90 days, surpassing traditional risk estimation methods. This advancement highlights the potential for AI to play a significant role in early cardiac care, especially for conditions like atrial fibrillation that are challenging to diagnose due to their intermittent nature. The technology promises to improve early detection and treatment, potentially preventing serious cardiovascular events in patients with this often undetected arrhythmia.

Cedars-Sinai research shows deep learning model could improve AFib detection Healthcare IT News