SBN Log Data is Not Effective as a Foundation for Prevention, Detection, Remediation or Analytics

Data drives virtually every aspect of modern business and industry, but data-dependent organizations face a significant challenge when it comes to harnessing the power of that data while safeguarding it against a long list of security threats. 

One popular solution is a log-based security program like SIEM (security information and event management) software. In a nutshell, log-based security solutions work by aggregating data related to network behavior and logging it for cross-reference against future network behavior. As the SIEM reviews ongoing behavior, unexpected and suspicious activity gets flagged for review by SOC teams. 

In a perfect world, SIEM logging would make life easier for security teams. It would take notice of every vulnerability and potential threat, and it would be smart and selective in flagging unusual network behavior. 

In the imperfect, real world, where SOC teams actually live, the SIEM log-based approach is fundamentally flawed. In fact, it tends to add more challenge to the workday for SOC teams, not less. 

What’s Wrong with Log Data?

The fundamental flaw that keeps SIEM from fulfilling the promises vendors promote is quite simple: when it comes to real-time threat prevention and detection, log data is inevitably incomplete, inappropriate and ineffective. 

By its very nature, a log based solution is only as current as its latest aggregation. Given the sprawling nature of today’s typical corporate network infrastructure, this approach is wholly insufficient. Log data will always be incomplete, pre-summarized, and prohibitively limited in its ability to give SOC teams insight into what is actually happening on a broader level of granularity for the summarized information. 

To overcome some of the limitations of these solutions, vendors offer a wide range of additive tools. For example, NTA tools add improved traffic analysis. However, when tools are stacked upon a foundationally flawed premise from the start, perceived improvements are not a true solution. 

These stacked solutions are like a house of cards, dependent on an underpinning of summary aggregate data that is outdated as soon as the aggregation is complete. At best, it’s an aggregate of data that was relevant during a snapshot in time. At worst, it’s an aggregate that does nothing for organizations for protecting against modern, coordinated cyberattacks and zero-day attacks. 

Then There Are the Surprise Bills…

Almost as if to add insult to injury, organizations are often hit with another serious flaw as SIEM customers: the often shockingly huge bills associated with the “hot/warm storage” these systems require to function. 

SIEM is reliant on organizational data, a constantly expanding resource for growing companies. Initial cost projections all too often fail to take into account the spiraling cost issue coming down the pike, especially for organizations poised to scale. 

Ultimately, a singular license can turn into a bill three to four times the initial cost at the end of year one. 

MixMode is the Antidote for the Unfilled Promises of SIEM

Because MixMode is not limited to log data analysis, organizations can identify and address real-time threats as well as network and operational configuration challenges. While an existing SIEM solution can create a situation where data storage needs to increase by 400 or 500 percent, MixMode actually decreases storage needs, often by more than 50 percent. 

MixMode saves its clients on the financial investment of SIEM across the board. Instead of relying on expensive warm storage solutions where data is always standing by, MixMode ingests and compresses raw data packets and sends them to cold storage — no labeling and no need to normalize data so it can be used for behavior analytics. 

MixMode creates a self-adjusting baseline of expected network behavior within a few days, one that serves as the foundation of a predictive alerting solution that gives organizations access to robust security features and a transparent, ethical pricing structure. 

Learn more about the MixMode solution and set up a demo today.  

MixMode Articles You Might Like:

Why Traditional Cybersecurity Tools Cannot Defend Against Zero-Day and No Signature Attacks

How AI is Contributing to Global Warming and What it Can Learn from Bitcoin

Incremental Stacking of Correlative Analysis Platforms Will Ultimately Prove Ineffective and Costly

A Modern SOC Should Not Be Entirely Dependent On Human Operators and Their Personal Experience

Maximize ROI with Greater Efficacy Using Unsupervised AI

How Self-Supervised AI Tackles Ambiguity in Network Security

*** This is a Security Bloggers Network syndicated blog from MixMode authored by Geoffrey Coulehan, Head of Sales. Read the original post at: https://mixmode.ai/blog/log-data-is-not-effective-as-a-foundation-for-prevention-detection-remediation-or-analytics/