การวิเคราะห์ - รายงานและบทสรุป

วิธีการวิเคราะห์ของเราประกอบด้วย:

• การเชื่อมโยงและการวิเคราะห์รูปแบบ
• แนวโน้มและการวิเคราะห์ทางเทคนิค
• แนวโน้มและการวิเคราะห์ทางวัฒนธรรม
• การวิเคราะห์ความผิดปกติและ Semiotic
• การวิเคราะห์เชิงคาดการณ์และการวิเคราะห์ภัยคุกคามที่เกิดขึ้นใหม่
• ข่าวกรองปัจจุบันคำเตือนเชิงกลยุทธ์และการประมาณการ

The following areas of AI-Infused Analytic Reporting is under research with several areas already used:

Stakeholder Requirement Gathering: Use Natural Language Processing (NLP) algorithms to analyze stakeholder interviews, extracting essential information requirements more efficiently, thereby expediting the initial stages of the analysis as a service model.

Prioritization Algorithms: Implement machine learning algorithms to assist in prioritizing intelligence targets and needs based on historical success rates and emerging threat landscapes.

Automated Collection Plan Review: Use AI to quickly review and validate the collection plan, enabling stakeholders to focus on core objectives and rapidly move to the execution phase.

Unified Data Collection and Analysis: Leverage AI for real-time data integration, ensuring that collection and analysis are not compartmentalized but directly engaged as a cohesive unit.

Smart Data Organization: Employ AI algorithms to automatically organize collected data, efficiently cataloging and prioritizing it, thus facilitating rapid changes to collection plans and advanced adversary targeting.

Automated Data Segmentation: Utilize machine learning to automatically segment useful information from non-relevant data, preparing it for the first review.

AI-Enhanced Briefing Generators: Implement Natural Language Generation (NLG) to automate the creation of different types of briefs like Platform Briefs, Adversary Briefs, and Executive Briefs, among others.

Intuitive Link and Pattern Analysis: Apply machine learning algorithms that identify correlations and patterns across disparate data sets, enhancing traditional link and pattern analysis techniques.

AI-Driven Trend and Technical Analysis: Use predictive analytics to identify emerging trends and techniques in cyber threats, complementing human-led technical analysis.

Cultural and Tendency Analysis: Use NLP to analyze cultural tendencies and sentiments in data, providing a deeper layer of understanding that could be critical for intelligence objectives.

Anomaly Detection for Semiotic Analysis: Implement anomaly detection algorithms to highlight unusual symbols or signs that could signify important intelligence cues.

Anticipatory Algorithms for Emerging Threats: Utilize machine learning to analyze data to predict emerging threats, aiding anticipatory analysis efforts.

การรายงานอัตโนมัติ: Use AI to automatically generate reports like Technical Intelligence Situation Reports and Sensitive Information Reports, which human analysts can review for finer details.

Dynamic Collection Plans: Implement AI to continually update and maintain the relevance of collection plans, incorporating adaptive learning algorithms to improve ongoing efforts.

Iterative Lifecycle Management: Integrate AI to streamline the iterative lifecycle of intelligence gathering and reporting, allowing for more adaptive, timely, and accurate intelligence outputs.