Compliance Verification via Negative Validation Testing
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Solution Overview
Problem
Current compliance testing methods lack assurance that implemented controls effectively protect assets from security risks, as they primarily rely on positive tests that do not validate the actual protective capabilities of controls.
Innovation Solution
The technique combines positive compliance tests with negative compliance tests, using machine-learning algorithms to derive a negative compliance test score and generate an assurance score that characterizes the effectiveness of controls in protecting assets, thereby increasing confidence in compliance test results.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Ease of operation
If only positive compliance tests are used to assess controls, then the ease of operation is improved, but the reliability of compliance assessment deteriorates
Solution Approach 1:
The patent applies inversion by introducing negative compliance tests that work opposite to traditional positive tests. While positive tests check whether controls are present and configured correctly, negative tests attempt to exploit vulnerabilities by simulating attacks. This dual approach ensures that controls not only appear to be in place but actually function to prevent security breaches, thereby resolving the contradiction between ease of testing and reliability of assessment.
2Measurement precision
If machine-learning algorithms and predictive analytics are integrated into compliance testing, then the measurement precision is improved, but the device complexity increases
Solution Approach 1:
The patent uses machine-learning algorithms and predictive analytics as intermediaries between raw compliance test data and final compliance scores. These algorithms process test results, identify patterns, and generate predictive compliance scores that more accurately reflect the effectiveness of controls. While this adds computational complexity, it significantly improves measurement precision by moving beyond simple pass/fail metrics to nuanced assessments of control effectiveness.
Data Source
AI summary
Methods and systems, including computer programs encoded on a computer storage medium, implement compliance testing to evaluate controls used to protect assets of a target system. A respective first score is generated for each control based on compliance tests performed to detect each of the controls at the target system. A compliance model is generated that integrates machine-learning algorithms to classify inputs corresponding to a compliance test and to enable predictive analytics of the compliance model using the classified inputs. The compliance model derives a negative compliance test (nCT) for each of the compliance tests by applying the predictive analytics to a data set that includes the first score for each control. An nCT is performed for each control detected at the target system and a second score is generated for each nCT. An assurance score characterizing effectiveness of the control is generated based on the first and second scores.