The Hidden Costs and Challenges of Log Data Storage Using a SIEM
Ultimately, MixMode found, the log-based SIEM approach resulted in five times the amount of data that needed to be stored, a cost that was passed along to the government entity.
Data Overload
How a Government Entity Switched to MixMode and Decreased Data Storage Costs by 50%
Data is the beating heart of every modern organization, but it’s only valuable when it’s accessible, understandable, and most importantly, protected.
Whitepaper: The Failed Promises of SIEM
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.
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.
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.
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.
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.