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COVID-19 has completely changed our world from six months ago, as we continue to battle the grave health implications, face extended stay at home orders, and grapple with the insurmountable ramifications on our economy. The pandemic has also forever changed the cyber threat landscape, with our workforce becoming more dispersed, and potentially more vulnerable, than ever as organizations switch out of the confines of their offices and move entire data streams to their laptops and home offices. On top of this, Salesforce has announced it is ending its Data Recovery service on July 31st, which is putting all of the data protection responsibilities, and the dire consequences that comes along with it, on the backs of the customer.
To address this current losing war with cyberattackers, the future of cybersecurity requires augmenting the current focus of “indicators of compromise” with “indicators of exposure & warning” in real-time. Where the measure would be to gauge the shift of incident management that would tilt on managing more incidents at warning stages than on compromise stages. It is imperative to build an AI engine to perform this very task as that would be the only way to perform in real-time, scale with the growing nature of cloud as well as to cover the evolving nature to attack scenarios.
As the head of information security for a technology company with more than a thousand (now mostly-remote) employees, the COVID-19 pandemic has been — among other adjectives — an educational experience. And while it hasn’t been completely smooth sailing, I believe one of the reasons we were able to transition so quickly to remote work with relatively few hiccups is that we established practices to withstand precisely this type of scenario long before the virus swept through our community.
U.S. Rep. John Katko introduced legislation to require the federal government to report to Congress on their preparation planning to address the effects of a potential COVID-19 resurgence.
Get to know James Carder, CSO at LogRhythm, who has more than 19 years of experience working in corporate IT security and consulting for the Fortune 500 and U.S. Government. At LogRhythm, he develops and maintains the company’s security governance model and risk strategies; protects the confidentiality, integrity and availability of information assets; and oversees both threat and vulnerability management as well as the security operations center (SOC). Carder previously led criminal and national security related investigations at the city, state and federal levels, including those involving the theft of credit card information and Advanced Persistent Threats (APT).
As the financial services industry moves toward an ever-greater dependence on technology, we must always keep an eye on the future to ensure that any new technological advancement or implementation delivers the same, if not better, benefits and risk management capabilities. One emerging area that has garnered a lot of attention in recent years is Distributed Ledger Technology (DLT). While DLT holds great promise, there is currently no clear path around how to implement the technology in a way that addresses documented and evolving security risks.
Counterfeiters do not take time off. At its core, counterfeiting preys upon our vulnerabilities and takes advantage of the average customer at any cost. This is particularly true right now during the coronavirus pandemic, the most inconvenient and vulnerable moment in generations. In the midst of mass shortages and colossal demands for certain products, especially in the health field, the counterfeit community has seen a golden opportunity. Over the past few months, tens of millions of new counterfeit products have been seized or identified on the web. These include fraudulent face masks, ventilators, disinfectants and testing kits.
As consumers increasingly turn to online shopping for essential and non-essential goods while at home, fraudsters have adapted their technique to use more sophisticated tactics against consumers, banks and merchants.
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