Federal agencies are dealing with more serious threats that are harder to detect than they’ve ever faced. When national security is at stake, your agency can’t afford to let these threats go unchecked.
That’s why manual analysis of your security data isn’t enough. Your data analysis tools need to tap into the power of machine learning in order to detect and deter threats at the agency level.
In this fifth and final post of our five-part series, we look into the critical role of machine learning for your data models and overall agency security.
In Part I, we discussed robust data collection tactics for stronger security decision-making; in Part II, we examined how to avoid common pitfalls when working with data dashboards; in Part III, we drilled into the importance of data quality for security decisions; and in Part IV, we dove into the predictive power of statistical modeling.
Tracking and predicting human behavior is a major aspect of federal agency security. Yet, human behavior is composed of millions of data points, and given how many personnel are employed at your agency, the data is simply too overwhelming for human-only analysis.
Instead of having humans do the majority of your security data analysis, you need to teach machines how to sift through your data, predict trends and generate needed insights.
Here are four principles of effective machine learning to keep in mind as you leverage this new capability for your agency security operation:
1. Start With Your Baseline
When creating or modifying your security data model, start by measuring a baseline. Your baseline might be made of behavioral or technical data points, but it should always signify a normal level of activity or behavior.
Then, train your machine learning algorithm to detect any data points that are beyond a standard deviation from your baseline. Once these outliers are detected, your model should send an alert to the appropriate security leader for further investigation.
2. Tap Into Bayesian Logic
While establishing a behavioral baseline is important, you also need to train your security models to adapt as that baseline changes. Using Bayesian logic, your model adjusts to new levels of “normal” as it receives additional data.
For example, a model tracking pedestrian traffic in front of a given security camera should trigger an alert if traffic rises or falls more than a standard deviation from the baseline. But if foot traffic continues to remain exceptionally high or low (due to an outside circumstance), a model with Bayesian logic would learn to accept the new baseline of “normal” behavior.
3. Learn To Quantify Risks
The purpose of using data science and machine learning for your agency security operations is to quantify risks. Eventually, your data models should be able to determine the probability of an occurrence and the monetary amount of risk from any given anomalous event.
Quantifying risk analysis is advanced data science, but robust machine learning algorithms help you tackle the challenge of risk probabilities with confidence.
4. Find The Signal In The Noise
Machine learning is merely a means to an end: detecting and deterring security threats at your federal agency. In order to detect those threats, you need to tune your machine learning tools to find precise signals of an oncoming threat – and not just the other “noise” of data happening at your agency.
Finding the perfect level of monitoring takes time. You don’t want to over-fit your model to a far-too-specific incident, nor do you want your models to monitor too broadly and miss a threat signal amidst the “noise.” Achieving that balance requires long-term practice and constant tuning.
Detecting, mitigating and managing security threats at your federal agency requires an approach that’s always adapting, changing and learning. By leveraging the power of machine learning, you stay one step ahead of any potential threats.
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Catch up with the rest of the elite data analysis series:
- Elite Data Analysis For Security I: Data Collection Tactics
- Elite Data Analysis For Security II: Data Dashboard Pitfalls
- Elite Data Analysis For Security III: Emphasizing Data Quality
- Elite Data Analysis For Security IV: Statistical Modeling