Solidifying security analytics with artificial intelligence knowledge graphs
Each successive instance of data compromises (and their escalating repercussions) is a veritable case study for the necessity of security analytics. With increasing regulations and new security threats amassing daily, the deployment ofuser behavior analytics may well become the most viable tool for protecting enterprise data.
In fact, security analytics is one of the more cogent drivers for the continuation of a centralized architecture in today’s heterogeneous data ecosystem. By aggregating analytics of user behavior from multiple endpoints—on premises, in the cloud, or even at the cloud’s edge—in a centralized location, organizations leverage big data’s scale to identify patterns of concern over countless nodes.
The advantages of security analytics exponentially increase with the deployment of Artificial Intelligence Knowledge graphs, which use advanced machine learning techniques for a bevy of boons. The overarching graph framework is ideal for centralizing the many different forms of knowledge related to security issues, which may include data about emerging threats, actual instances of breach, or anything even remotely related to data compromises.
Even better, these repositories store data about such factors as they apply to the enterprise and to external entities, so organizations can reap lessons learned from the more prominent breaches of our times. Their smart data approach is designed to discern relationships between data for specific functions (such as security concerns), while enriching them with AI enables prescriptive capabilities for mitigating vulnerabilities, threats, and breaches—before they occur.
The knowledge graph framework connects data on a semantic graphwith a linked data approach ideal for synthesizing and detecting relationships in even disparate data. By standardizing data with uniform models, taxonomies, and classifications, organizations can align data of immensely different structures, sources, and formats to determine how they relate to a predefined objective.
For security, organizations could compile the security clearances and access control privileges of employees to account for internal vulnerabilities and exfiltration factors. These data can be aligned with those of customer facing applications for real-time behavioral monitoring of such systems. Additionally, any previous security issues related to specific malware or fishing attacks, as well as successful or unsuccessful hacking attempts, can be compiled in this graph. The objective is to link together all data pertaining to security to aggregate, then analyze, the potential for misbehavior.
The greater value comes from incorporating knowledge external to the enterprise for profound insights.
