Uncovering the Truth about Six Big Data Security Analytics Myths

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Traditional Discovery

Myth #4: Traditional approaches support discovery of advanced threats.

Traditional security monitoring and response solutions, in which organizations have significant investments, can be easily modified to provide the analytics needed to detect advanced threats.

Why it's a myth: Traditional solutions are built on technologies that cannot scale to the volumes and variety of data being generated in modern organizations. They are rule-based, which is ineffective for unknown attacks. The inability to scale and the lack of self-learning analytics operating on Big Data means that they aren't effective against advanced, multi-stage attacks.

Cyber threats are becoming extremely sophisticated, as evidenced by the many high-profile breaches over the last few years. Organizations are confronting a new reality where they must accept that they are likely to be impacted, despite their best attempts to keep these threats out altogether. They need quicker and better ways to discover, investigate and remediate these threats. Marrying Big Data with machine learning can help address this challenge by providing security professionals with the Big Data security analytics (BDSA) they need to thwart the bad guys.

Without a doubt, when BDSA is used correctly, it is extremely beneficial to an organization. However, there are many false claims around the capabilities of BDSA. When considering BDSA solutions, analysts need to carefully evaluate these capabilities and determine whether their organizations' needs for detection of attacks on the inside and incident response are being met. In this slideshow, John Dasher, vice president of marketing at Niara, a cybersecurity company focused on Big Data analytics, has identified six common myths to consider when deploying BDSA solutions.

 

Related Topics : Unisys, Stimulus Package, Security Breaches, Symantec, Electronic Surveillance

 
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