Anomaly Detection in Software Development Pipelines

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Solution Overview

Problem

Current software development systems lack a comprehensive solution to identify and mitigate risky developer behavior across complex development pipelines, leading to undetected security risks and potential code leakage.

Innovation Solution

A system and method utilizing generative algorithms and reinforcement learning to detect, predict, and remediate anomalous behaviors in software development, by analyzing time-series data from digital identities and development tools, and employing a risk-anomaly matrix to associate anomalies with risk tags.

Engineering Contradictions & Design Principles

VSEngineering Contradiction Analysis

1Reliability

If comprehensive monitoring of developer behavior across multiple digital identities is implemented, then security risk detection capability is improved, but system complexity and computational resources increase

Engineering Contradiction:

Improvesecurity risk detection capabilityVSAvoidsystem complexity

Core Design Contradiction:

ReliabilityVSDevice complexity

Solution Approach 1:

The system segments developer behavior monitoring into distinct digital identity profiles across multiple development tools (source code repositories, service management tools, IAM tools). Each identity's behavior is tracked separately through time-series data collection, allowing comprehensive monitoring while organizing complexity into manageable segments.

Inventive Principle:

Principle #1Segmentation

Solution Approach 2:

The patent introduces an intermediary anomaly detection system that collects time-series data from multiple digital identities and development tools, processes this data through machine learning models, and generates anomaly scores. This intermediary layer consolidates the complexity of cross-tool monitoring into a unified analysis platform.

Inventive Principle:

Principle #24Intermediary (Mediator)

2Measurement precision

If time-series data from multiple digital identities is analyzed to detect anomalies, then behavioral anomaly detection accuracy is improved, but data processing time and computational resources increase

Engineering Contradiction:

Improvebehavioral anomaly detection accuracyVSAvoiddata processing time

Core Design Contradiction:

Measurement precisionVSLoss of time

Solution Approach 1:

The system performs preliminary actions by continuously collecting and storing time-series data from digital identities in advance of anomaly detection needs. Historical behavior patterns are pre-processed and organized by digital identity and development tool, enabling rapid anomaly detection when needed without extensive real-time processing delays.

Inventive Principle:

Principle #10Preliminary action

Solution Approach 2:

The patent replaces traditional mechanical rule-based anomaly detection with machine learning models that automatically learn behavioral patterns from time-series data. The ML models process multiple digital identity data streams simultaneously, improving detection accuracy while reducing manual processing time through automated pattern recognition.

Inventive Principle:

Principle #28Mechanics substitution (Replace mechanical system)

3Reliability

If machine learning models are trained on historical anomaly data, then predictive anomaly detection capability is improved, but training data requirements and model complexity increase

Engineering Contradiction:

Improvepredictive anomaly detection capabilityVSAvoidtraining data requirements

Core Design Contradiction:

ReliabilityVSQuantity of substance

Solution Approach 1:

The machine learning models are designed with multi-functionality to handle multiple digital identities, development tools, and anomaly types within a single training framework. The models learn universal behavioral patterns that apply across different contexts, reducing the need for separate training datasets for each identity or tool while maintaining predictive capability.

Inventive Principle:

Principle #6Universality (Multi-functionality)

4Reliability

If cross-identity behavioral patterns are analyzed to detect composite risks, then holistic risk assessment is improved, but analytical complexity and computational overhead increase

Engineering Contradiction:

Improveholistic risk assessment capabilityVSAvoidanalytical complexity

Core Design Contradiction:

ReliabilityVSDevice complexity

Solution Approach 1:

The system merges behavioral data from multiple digital identities associated with the same developer into unified anomaly detection analysis. By combining time-series data across identities and tools, the system detects composite risks and cross-identity patterns that would be invisible when analyzing identities in isolation, achieving holistic risk assessment through data consolidation.

Inventive Principle:

Principle #5Merging (Combining)

Data Source

PatentUS20250117492A1Method and program product for identifying behavioral anomalies and risk factors in software development

Publication Date: 2025.04.10 BLUEFLAG SECURITY INC

AI summary

A method and computer-implemented process for identifying behavioral anomalies and risk factors is disclosed. The method includes (A) obtaining, by a risk assessment module, first data associated with activity information of a digital identity with respect to a digital development tool; (B) associating, by the risk assessment module, second data with a first risk tag based on a risk-anomaly matrix; (C) generating, by the risk assessment module, a first predicted anomaly using a machine learning module trained using a first training set, wherein the first training set comprises the second data and the first risk tag; (D) associating, by the risk assessment module, the first predicted anomaly with a second risk tag based on the risk-anomaly matrix; and, (E) transmitting, by the risk assessment module, a notification associated with at least one of (i) the anomaly and (ii) the predicted anomaly, and the digital identity.