Mixmode Blog
Lorem ipsum dolor sit amet, consectetur adipiscing elit.
How Data Normalization in Cybersecurity Impacts Regulatory Compliance
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.
Read More
Webinar: The Failed Promises of SIEM – What’s Next For Cybersecurity
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.
Read More
3 Reasons Why a Rule-Based Cybersecurity Platform Will Always Fail
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.
Read More
Why Data Overload Happens and Why It Is a Problem for Cybersecurity Teams
Handling and managing data today has become unwieldy for IT teams on multiple fronts, but the security impact is especially troubling.
Read More
Why SIEM Has Failed the Cybersecurity Industry
The time required for data processing, transition, aggregation, and the normalization does not allow real-time threat detection using today’s SIEM solutions. The only beneficiary of security through log aggregation is the SIEM vendor.
Read More
Data Overload Problem: Data Normalization Strategies Are Expensive
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.
Read More
What is Predictive AI and How is it Being Used in Cybersecurity?
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.
Read More
Whitepaper: The Data Overload Problem in Cybersecurity
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.
Read More
Magnify Podcast: Discussing the New Normal with AI Based Cybersecurity Specialists, MixMode
Geoff Coulehan, MixMode’s Head of Strategic Alliances, joined Secrutiny’s “Magnify Podcast,” to discuss the priorities CISOs should focus on to better protect their now-remote team of employees.
Read More