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Organizations continue to face increasingly sophisticated cyber threats that require a proactive and adaptive approach to cybersecurity. As the volume and complexity of security data grows, security professionals are turning to advanced AI technologies to enhance their capabilities and stay ahead of emerging threats.
Overall, Black Hat 2023 demonstrated that the cybersecurity landscape continues to evolve rapidly. The event highlighted both the threats posed by emerging technologies like AI, as well as the potential for new defensive techniques.
The constant threats posed by hackers, state-sponsored cyber attacks, and malicious actors have prompted the creation of cutting-edge strategies to fortify our nation’s cyber defenses. One such response to this growing concern comes in the form of an annual large-scale international cybersecurity exercise.
The financial services industry handles vast amounts of highly sensitive financial information, including customer data, transaction records, and intellectual property, making them a prime target for cyber attacks.
A new mass ransomware attack is making headlines. As the Washington Post reports in a recent article, “The latest mass ransomware attack has been unfolding for nearly two months,” major U.S. corporations have fallen victim to an attack that may have origins dating back to late January.
MixMode’s Chief Strategy Officer, Matt Shea was invited to provide the opening keynote address, setting the stage for discussions on how businesses and municipalities can better protect their networks and environments from cyber attacks.
Shannon Lawson, CISO for the City of Phoenix, and Geoffrey Coulehan, Head of Sales for MixMode recently joined forces for a fireside chat at the Evanta CISO Summit in Phoenix, AZ. Lawson and Coulehan have been partnering together for almost three years to transform the City’s cybersecurity initiatives and Security Operations Center (SOC).
In the report, 451 Research explains why security analytics needs to include advanced Third-Wave AI, which autonomously learns normal behavior and adapts to constantly changing network environments, to address the next generation of cyberthreats and increase SOC productivity.
In our newest case study, “How a Major U.S. City Rapidly Modernized Its Cybersecurity Defenses,” we share how the City cut its cyber tool footprint in half, gained visibility into advanced foreign adversary attacks, and greatly improved the productivity of its SOC staff.
When it comes to cybersecurity, more is not always better. As new solutions have emerged in response to rapidly evolving technology and increasingly sophisticated cyber attacks, SOCs have added tool after tool to their arsenals.
Experts have warned that the Russia-Ukraine conflict poses an unprecedented cyber risk for U.S. organizations as well as State and local governments and municipalities.
It’s no surprise that organizations are pouring resources into their security approaches, from investments into hardware and software and significant increases in Cybersecurity professional hiring. In fact, industry watchers expect organizations globally to contribute to $1.75 trillion in cumulative spending on Cybersecurity between 2021 and 2025.
Monthly reports that lack relevant details about an organization’s true risk level are insufficient and not representative of the further steps an organization should take to protect itself. This approach leaves organizations feeling secure against the threat of ransomware while they are actually left exposed to potentially expensive, wide-scale damage.
Ransomware attackers aren’t always after the biggest fish. In fact, so-called “mid game hunting” — as opposed to “big game” hunting — where attackers identify smaller targets that are less likely to trigger a legal or governmental response, appears to be on the rise.
When Cybersecurity teams assume that log-based platforms are the best available network security solutions, they have made a decision to invest in “bad gas.” These products will cost more and deliver less than systems enhanced by context-aware AI.
Anomaly detection, the “identification of rare occurrences, items, or events of concern due to their differing characteristics from the majority of the processed data,” allows organizations to track “security errors, structural defects and even bank fraud,” according to DeepAI and described in three main forms of anomaly detection as: unsupervised, supervised and semi-supervised. Security Operations Center (SOC) analysts use each of these approaches to varying degrees of effectiveness in Cybersecurity applications.
2020 will be remembered most as the year the world was swept up in the COVID pandemic. Dig a little deeper and you’ll find another alarming news story: 2020 was a record breaking year on the Cybersecurity front. There was more data lost in breaches and a higher number of cyber attacks than ever before.