Analysis - Reports and Briefs
Targeted Analysis Driving Clear and Concise Reports and Briefs
We know that intelligence analysis needs significant skill. We know analysis is not the easiest thing to do. That is why we offer the Analysis as a Service model for our clients. Our collection uses information requirements directly taken during stakeholder interviews. We then prioritize with you to ensure clear targeting and need. Your review and signature on the collection plan is a core objective prior to execution. We use detailed and documented research methods that:
Treadstone 71 Collection and Analysis is not compartmentalized but directly engaged as a single unit. Continuous feedback loops and constant communication ensure rapid changes to collection plans and advanced adversary targeting.
- Organizes collected data
- Catalog and prioritize
- Segments useful information from non
- Prepares the data for first review
- Platform Brief
- Adversary Brief
- Current Intelligence Brief Operational Intelligence
- Brief Event Brief
- Intelligence Estimate
- Daily Intelligence Summary
- Executive Brief
- Stakeholder Brief Intelligence Advisories
- Baseline Intelligence Targeted Adversary Reports
- Technical Intelligence Situation Reports, Tactical
- Sensitive Information Reports Estimative
- Intelligence Baseline Intelligence Summary
- Warning/Threat Intelligence Flash Precedent
Our methods followed traditional structured analytic techniques blended with intuition requiring patience, perseverance, aptitude and skills. Attributes highly sought after in the industry but hard to find. Treadstone 71 drives research and extracts data from both the surface internet and the darknet. The analysis we perform and the reports we supply contextually align to your priority intelligence requirements while creating trackable collection plans. The plans provide a living roadmap that maintains collection relevance incorporating critical thinking and intellectual traits.
Our analysis methods include:
- Link and Pattern Analysis
- Trend and Technical Analysis
- Tendency and Cultural Analysis
- Anomaly and Semiotic Analysis
- Anticipatory Analysis and Analysis for Emerging Threats
- Current, Warning, Strategic, and Estimative Intelligence
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.
Automated Reporting: 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.
We collect - We organize - We decompose - We prioritize - We analyze - We think - We report - We deliver - Iterative lifecycle methods incorporating objective analysis with intuition and structured methods of analysis - Since 2002
- Analysis - Reports and Briefs
- Adversary Baseball Cards
- Cyber Intelligence Capability Maturity Model Assessments
- OPSEC Assessment - Intelligence Preparation of the Cyber Battlefield
- Interim Head of Intelligence
- Internal Intelligence Communities of Interest
- Intelligence Requirements
- Threat Intelligence Platform Selection and Rollout
- Data Sheet Request
- PIRs - How to build them properly