Technical papers covering architectures, methods, evaluations, applied AI systems, and implementation detail.
Publications
Whitepapers and strategic briefs for usable AI judgment.
Whitepapers and strategic briefs from Evening Star AI. Research is published as short, decision-focused work for builders, security teams, leaders, and operators. Each publication connects a research program to practical AI systems that can be tested, explained, deployed, and governed.
Publication Types
Publication Types
Not every Evening Star AI publication is meant to be a dense technical paper. Some are whitepapers. Others are strategic briefs. The goal is to publish useful thinking that can be read, tested, debated, and applied.
Idea-driven papers for leaders and builders: frameworks, operating principles, AI strategy, and decision-focused analysis.
Evening Star AI Operating Principles
A concise statement of how Evening Star AI should think, build, and operate: observe context, reveal change, reason about impact, and help humans move.
Intended audienceFounders, technical leaders, AI builders, operators, and collaborators evaluating the institute's operating philosophy.
The Evening Star AI Engine
Technical architecture for application-agnostic operational intelligence: baselines, detector ensembles, drift, attribution, confidence, and governed action.
Intended audienceAI engineers, security architects, platform builders, and technical executives evaluating reusable anomaly-intelligence systems.
Purple Radar: AI-Driven Vulnerability Intelligence
A decision-focused model for vulnerability prioritization, exposure analysis, exploitability signals, risk scoring, and evidence-first remediation planning.
Intended audienceSecurity teams, vulnerability managers, CISOs, cyber operators, and leaders responsible for risk-prioritized remediation.
Purple Firefish: An AI Security Gateway for LLM Applications
A governed gateway pattern for prompt injection, jailbreak pressure, indirect prompt attacks, adversarial inputs, risky tool use, and unsafe model outputs.
Intended audienceAI application teams, security engineers, red teams, GRC leaders, and operators deploying LLM workflows.
Candles Edge: AI Decision Support for Market Signals
A market-intelligence proving ground for turning OHLCV features, volatility expansion, regime shifts, anomaly markers, and decision signals into usable human judgment.
Intended audienceMarket operators, product builders, decision-system designers, and analysts studying noisy, fast-moving environments.
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