It’s widely acknowledged in today’s cyber-security environment that insider threats are rapidly becoming the most significant risk factors across organizations and industries. Each and every agent in your organization – your leaders, employees, contractors and partners – presents potential for data loss.
Internal breaches run the gamut from malicious, methodical attacks to casual carelessness. Whatever the intent or source behind the threat, the damage is the same. You may think that you can’t identify who will intentionally or inadvertently compromise your network security. You may think that your best plan is a good defense. But is that the best solution for data loss prevention?
Imagine if you could predict and profile precisely who in your network presents a security risk and how these individuals will most likely sabotage your company.You’d be in a position to proactively predict and prevent problems before they happen. Essentially, your best defense becomes a good offense. And today, a good offense includes predictive analytics and big data analysis combined with a neural network.
Far too many organizations have no way to identify and contain internal agents that present risk. It’s like looking for needles in a haystack. To get started with building a better security framework, it’s critical to be able to authenticate the identity of your users and put user provisioning protocols in place to specify permissions around access at an individual, granular level.
For organizations that have implemented automated identity authentication and user provisioning capabilities, the next step is leveraging predictive analysis to track user behavior patterns over time. The ability to capture this kind of data allows you to detect risk associated to a specific individual and create risk profiles for specific users.
Once you’ve implemented artificial intelligence software to track individual behaviors, you can use your findings to cross-tab patterns across users to correlate specific behaviors to increased organizational risk. The beauty of predictive analysis tools combined with the ability to process huge volumes of data can limit intellectual property theft, and assure data loss prevention.
Predictive analytics in a neural network offers unique advantages, including improved accuracy over traditional statistical analysis methods, a unified approach to a variety of predictive analytics problems, fewer assumptions and automated management.
Leveraged fully, this artificial intelligence can gather information around specific users and behavioral patterns that correlate to increased risk. And the more data the better – while other analytical tools buckle under the glut of huge data volume, a predictive neural network by definition becomes more effective with more data points. This allows you to leverage the power of big data to build richer intelligence frameworks over time. And when your intelligence frameworks are richer, your security safeguards are more agile and adaptive.
When you combine automate identity authentication, user provisioning and behavior analytics with these predictive neural networks, you ensure your critical security initiatives are effective against insider threats. As a result, identifying those malicious needles among the stacks and stacks of harmless hay, becomes and easy chore.