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Russian Hack of U.S. Federal Agencies Shine Spotlight on SIEM Failures in Cybersecurity

Adversarial AI, Blog, Data Breach, Network Baselines, Network Detection and Response, Network Security, Network Traffic Analysis, Predictive AI, Self-Supervised AI, SIEM, Third-Wave AI, Threat Detection / By

In what the New York Times is calling, “One of the most sophisticated and perhaps largest hacks in more than five years,” malicious adversaries acting on behalf of a foreign government, likely Russian, broke into the email systems of multiple U.S. Federal agencies including the Treasury and Commerce Departments.

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Recent Ransomware Attacks on U.S. Hospitals Highlight the Inefficiency of Rules-Based Cybersecurity Solutions

Adversarial AI, Blog, Data Breach, Network Baselines, Network Detection and Response, Network Security, Network Traffic Analysis, Predictive AI, Self-Supervised AI, Third-Wave AI, Threat Detection / By

A number of recent high profile ransomware attacks on U.S. hospitals have demonstrated the urgency for organizations, municipalities, and critical services to take a proactive approach to protecting networks with a predictive AI solution.

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Stop Patching Leaks in Your Cybersecurity Boat: A Streamlined Cybersecurity AI Solution to Adversarial Attacks

Adversarial AI, Blog, Network Baselines, Self-Supervised AI, Third-Wave AI / By

At MixMode our one algorithm is capable of catching any anomaly that may appear on the network. In contrast, other security programs rely on a reactive method of patching and constantly adding to their algorithms each time a hack occurs so that the network learns what to look out for.

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The Case Against Using a Frankenstein Cybersecurity Platform

Blog, Network Baselines, Network Detection and Response, Network Security, Network Traffic Analysis, Predictive AI, Self-Supervised AI, SIEM, SOC, Unsupervised AI / By

The cybersecurity market has, simply put, been cobbled together. A tangled web of non-integrated systems and alerts from siloed systems. Enterprises are now being forced to utilize a “Frankenstein” of stitched together tools to create a platform that might cover their security bases.

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The Evolution of SIEM

Blog, Network Detection and Response, Network Security, Self-Supervised AI, SIEM, Whitepaper / By

It should be noted that SIEM platforms are exceptionally effective at what they initially were intended for: providing enterprise teams with a central repository of log information that would allow them to conduct search and investigation activities against machine-generated data. If this was all an enterprise cybersecurity team needed in 2020 to thwart attacks and stop bad actors from infiltrating their systems, SIEM would truly be the cybersecurity silver bullet that it claims to be.

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Whitepaper: The Failed Promises of SIEM

Blog, Data Overload, False Positives, Regulations & Compliance, Self-Supervised AI, SIEM, SOC, Whitepaper / By

The fundamental SIEM flaws lie in the platform’s need for continual adjustment, endless data stores, and a tendency to create an overwhelming number of false positives. When organizations instead turn to a next-generation cybersecurity solution, which predicts behavior with an unsupervised (zero tuning) system, they are poised to save on both financial and human resources.

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Webinar: The Failed Promises of SIEM – What’s Next For Cybersecurity

Blog, Self-Supervised AI, SIEM, SOC, Uncategorized, Webinar / By

The Security Operations Center (SOC) of today is fundamentally flawed. Currently enterprise cybersecurity spend is higher than ever, but despite multi-million dollar cybersecurity investments, organizations remain vulnerable to attacks. One of the major reasons for this is legacy SIEM deployments. More spend does not equal more security.

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3 Reasons Why a Rule-Based Cybersecurity Platform Will Always Fail

Blog, False Positives, Network Baselines, Network Security, Network Traffic Analysis, Predictive AI, Self-Supervised AI, SIEM, Zero Day Attacks / By

When it comes to advancements in cybersecurity, rule-based systems are holding the industry back. Relying on humans to constantly input and label rules in order to detect and stay ahead of threats is a bottleneck process that is setting security teams up for failure, especially with tools like SIEM, NDR, and NTA.

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Data Overload Problem: Data Normalization Strategies Are Expensive

Blog, Data Overload, Network Detection and Response, Network Security, Network Traffic Analysis, Self-Supervised AI, Whitepaper / By

Financial institutions spend five to ten million dollars each year managing data. A recent Computer Services Inc (CSI) study reveals that most banks expect to spend up to 40 percent of their budgets on regulatory compliance cybersecurity, often adopting expensive data normalization strategies.

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What is Predictive AI and How is it Being Used in Cybersecurity?

Blog, Network Detection and Response, Predictive AI, Self-Supervised AI, Threat Detection / By

The predictive AI field of machine learning collects, analyzes, and tests data to predict future possibilities. AI’s neurological network is patterned on the human brain. But AI works on a scale that goes far beyond what is humanly possible. The top uses for predictive AI technologies to protect sensitive data and systems are in network detection and response (NDR), threat detection, and cybercrime prevention.

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Whitepaper: The Data Overload Problem in Cybersecurity

Blog, Data Overload, Self-Supervised AI, SOC, Whitepaper / By

The very nature of data is its infinite capacity for growth. For security teams at large, highly integrated and complex enterprises like financial services institutions, that growth can quickly become unwieldy when the approach is to store, normalize and prepare all of this data in order to extract value.

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Guide: How to Choose an AI-Based Cybersecurity Platform

Blog, Context-Aware AI, Network Security, Predictive AI, Self-Supervised AI, SOC, Unsupervised AI, Whitepaper / By

Most cybersecurity vendors today tout some form of “Artificial Intelligence” as an underlying mechanism for the differentiation of their product among the market. But if everyone is saying they have AI, and everyone is also claiming theirs is the “best,” how can they all be telling the truth?

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Deep Dive: How much time do security teams spend labeling with Supervised Learning?

Blog, Self-Supervised AI, Unsupervised AI / By

Many CISOs and SecOps teams were faced with a gut-wrenching choice: addressing the operational challenges of keeping workers connected, or shoring up vulnerabilities before hackers exploited them. Both options involved time-consuming, repetitive, manual work.

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Why a Platform With a Generative Baseline Matters

Blog, Network Baselines, Network Detection and Response, Network Security, Network Traffic Analysis, Self-Supervised AI, Whitepaper / By

MixMode creates a generative baseline. Unlike the historically-based baselines provided by add-on NTA solutions, a generative baseline is predictive, real-time, and accurate. MixMode provides anomaly detection and behavioral analytics and the ability to suppress false positives and surface true positives.

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Why The Future of Cybersecurity Needs Both Humans and AI Working Together

Blog, Context-Aware AI, Network Detection and Response, Network Security, Network Traffic Analysis, Predictive AI, Self-Supervised AI, SOC, Threat Detection, Unsupervised AI, Zero Day Attacks / By

A recent WhiteHat Security survey revealed that more than 70 percent of respondents cited AI-based tools as contributing to more efficiency. More than 55 percent of mundane tasks have been replaced by AI, freeing up analysts for other departmental tasks.

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