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Data Overload

The AI Advantage: Mitigating the Security Alert Deluge in a Talent-Scarce Landscape

AI, artificial intelligence, Blog, Data Overload, Ponemon, Self-Supervised AI, Third-Wave AI / By

The cybersecurity landscape is under siege. Organizations are bombarded by a relentless barrage of security alerts, often exceeding a staggering 22,111 per week on average. While Artificial Intelligence (AI) has emerged as a powerful tool to manage this overwhelming volume, its effectiveness isn’t without limitations, as vendors flood the market with false advertising and promises.

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Webinar: Achieving Security at Big Data Scale – Why Organizations are Struggling with Legacy Tools and The Promise of Generative AI

Blog, Cloud Security, Data Overload, generative AI, Webinar / By

Cybersecurity veterans discuss the challenges large enterprises in the Fortune 1000 face in scaling security monitoring and threat detection across big data environments.

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eBook: The Inefficiencies of Legacy Tools – Why SIEMs Alone Are Ineffective At Detecting Advanced Attacks

Alert Reduction, Blog, Data Overload, eBook, False Positives, SIEM / By

Relying solely on legacy Security Information and Event Management (SIEM) technology is no longer sufficient to protect enterprise organizations from the rising amount of modern, ai-developed, sophisticated cyberattacks. In our newest eBook, we examine the limitations of SIEMs and emphasize the need for an AI-driven dynamic threat detection and response platform.

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The 2 Questions Cybersecurity Clients Ask the Most When Seeking a Trustworthy Provider

Blog, Cybersecurity News, Data Overload, Network Detection and Response, SOC, Threat Detection / By

When businesses trust providers to address needs like cybersecurity, they are entrusting vital intellectual property, the private data of employees, consumers, and others, and often, the very infrastructure the business requires to operate.

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SANS First Look Report: Self-Supervised Learning Cybersecurity Platform for Threat Detection

Blog, Data Overload, Data Storage Cost, Self-Supervised AI / By

The SANS Institute recently released an analyst First Look Report on MixMode titled, “Self-Supervised Learning Cybersecurity Platform for Threat Detection.” Matt Bromiley, Senior Security Analyst at SANS and author of the report, explores the barriers SOCs face to conquering the vast amounts of data generated by modern enterprises and the solutions that MixMode provides for detecting and investigating threats including our utilization of self-supervised AI for cybersecurity.

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Can Your Cyber Tools Monitor Any Stream of Data?

AI, Blog, Data Overload, Self-Supervised AI, SIEM, Third-Wave AI / By

It’s the open secret no one’s talking about — too many cybersecurity solutions in the marketplace stand no chance of providing comprehensive coverage because they are incapable of handling data arising from all sources. Many available solutions are effectively legacy platforms hiding within fresh marketing packages.

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As Enterprises Embrace 5G, AI-Enhanced Cybersecurity Emerges as Top Security Priority

5G, Blog, Cybersecurity News, Data Overload, Predictive AI, Self-Supervised AI, Third-Wave AI / By

The much-anticipated fifth generation (5G) of broadband cellular technology has arrived, ushering in unprecedented network speed and connectivity. The tech is also spurring innovation into new tech solutions to meet an ever-growing appetite for instant, reliable connectivity, often, faster than most enterprise Cybersecurity teams can handle. If there was ever a time for AI to deliver on the promises made by Cybersecurity platform vendors, it’s now.

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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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How Data Normalization in Cybersecurity Impacts Regulatory Compliance

Blog, Data Overload, Regulations & Compliance, SIEM, SOC, Whitepaper / By

Complying with privacy regulations requires all organizations to have access to data on demand, wherever it lives on a network. With the unfathomable amount of data managed by most organizations operating in the finance space today, it can become a significant challenge to locate specific data across legacy systems and networks with countless connections online and off.

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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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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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