Leveraging Analytics to Enhance Data Access Control - Satori

Leveraging Analytics to Enhance Data Access Control

As data volumes and users continue growing, organizations are struggling to maintain and constantly update traditional static methods like role-based access control (RBAC). This issue has grown to such a large scale that nearly half of all organizations provide employees with more privileges than they need to complete their job.

Analytics offers a powerful solution by enabling dynamic, intelligence-driven access policies. Leveraging techniques like predictive modeling, behavior analysis, and risk detection algorithms allows organizations to continuously optimize their data access governance.

To help you identify where you can use analytics for your data access control, this article covers:

Analytics Models for Access Control

Traditional access control methods depend on humans manually reviewing and assigning access rights as employees join, move between roles, and eventually leave an organization. This makes it challenging to keep up with access needs in real time.

Analytics provides a powerful complement to traditional access control by enabling dynamic, data-driven access control systems. Pairing analytics with access control helps identify potential risks by examining how users access and use data. These insights allow organizations to optimize their access policies to enhance security.

Common analytical data access management techniques include:

Network and graph analysis: Models user relationships and data access as a network graph to identify unusual or risky pathways. Algorithms flag access occurring along unexpected graph edges.
Anomalous access detection: Applies machine learning models to user behavior patterns. The models are trained on normal access patterns and flag significant deviations as anomalous.
Predictive analytics: Forecasts user access needs based on role changes, project assignments, and other HR data. Access permissions can then be provisioned automatically before users request them.
Sentiment analysis: Uses natural language processing to scan internal communications. Risky behaviors and disgruntled mindsets are identified based on emotional sentiment in emails, chats, etc.

How Can Analytics Improve Your Access Control?

Implementing analytics delivers multiple benefits for securing data access including:

Understanding User Access and Behavior

Data intelligence and analytics deliver visibility into how users interact with data across an organization. This allows for the creation of behavioral baselines to better distinguish normal and abnormal activity per user. Anomalous behaviors like accessing data outside of normal working hours or downloading unusually large amounts of info can trigger alerts for investigation.

Identifying At-Risk Permissions

By mapping out all existing access permissions across systems and data sources, analytics algorithms can identify and quantify risks such as dormant accounts, over privileged access, and segregation of duties violations. Analytics models can also compare user permissions to peer groups to detect inappropriate access.

Enhancing Access Policies

Using analytics to enhance your data access policies enables surgical precision when refining policies rather than broad-brush approaches. Analytics provides the insights needed to keep access policies aligned with actual user needs. Monitoring user activity patterns allows granting access when new needs arise and revoking it when no longer required.

Forecasting Access Needs

Predictive analytics can leverage data on future organizational plans to forecast which users will require access to which data resources. Some ways predictive models can anticipate future access needs include:

Analyzing upcoming projects and staffing assignments to predict required data access
Identifying new roles and responsibilities that will be created due to reorganizations
Modeling the progression of an employee’s career to forecast needs as they take on new roles
Detecting trends in how access needs evolved for similar past projects and initiatives
Processing HR records, calendar appointments, and emails to uncover planned events that necessitate new data access
Considering employee locations and schedules to determine probable access needs

Best Practices for Using Analytics to Enhance Your Data Access Control

To maximize the value of analytics for access control, keep these best practices in mind:

Continuously Tune Analytics Algorithms

The algorithms powering analytics models require ongoing tuning and supervision. Tracking accuracy metrics over time and adjusting models accordingly is critical for avoiding false positives or negatives. Plan to regularly update algorithms as database activity evolves.

Test the Accuracy of Analytics Over Time

Don’t take analytics model accuracy for granted. Rigorously test models on an ongoing basis to ensure they are detecting access risks as expected. Some ways to test include injecting anomalous activity data to see if caught and manually validating a sample of model findings.

Automate Where Possible But Maintain Human Oversight

Look for opportunities to automate data access policy changes prompted by analytics insights. But, human review is still essential before enacting high-risk policy adjustments like revoking permissions. Analytics should augment human oversight, not replace it.

Additional Considerations

While an invaluable tool, analytics-driven access control does come with a few caveats including:

Privacy concerns: Collecting user behavior data for analytics models may be invasive. Organizations should conduct privacy impact assessments and implement adequate consent procedures.
Biased algorithms: Models can inherit human biases or make unfair correlative assumptions. Continual bias testing and adjustment are critical.
Over-trusting models: Access decisions should require human review since no model is perfect. Models provide support, not absolute answers.
Adversarial manipulation: Bad actors may attempt to trick models by tweaking behavior patterns. Robust validation can help detect such manipulation.
Poor data quality: Garbage in, garbage out. If input data like user behavior logs are incomplete or inaccurate, model outputs will suffer.
Increased complexity: Advanced analytical techniques require specialized expertise. Plan for added training and recruiting costs.

Despite these caveats, analytics can provide invaluable visibility into access risks while streamlining access approvals. When implemented effectively, you can minimize the effect of these caveats to get the most out of analytics-driven access control.

Conclusion

Implementing analytics can significantly strengthen data access control and prevent breaches. However, care should be taken to regularly validate analytical model accuracy and maintain human oversight of policy changes. Organizations should also conduct privacy reviews when collecting additional user behavior data.

With appropriate data governance, analytics can create evolving access control systems that meet today’s complex security challenges. The insights delivered allow organizations to get ahead of risks rather than merely reacting.