Longer technical papers covering architectures, methods, evaluations, applied AI systems, and implementation detail.
Publications
Whitepapers and strategic briefs from Evening Star AI.
These papers are written for builders, security teams, leaders, and operators who need clear thinking about AI security, anomaly detection, software assurance, automation, and technical decisions.
The library separates core Evening Star papers, applied-lab work, and external public-sector pieces so readers can tell what kind of argument they are reading.
For the evaluation standard behind the library, see the Evening Star AI methodology.
Formats
Publication formats
Some pieces go deep into architecture and methods. Others are shorter briefs meant to clarify a problem, frame a decision, or explain why a pattern matters.
Shorter papers for leaders and builders: operating principles, strategy, decision analysis, and practical patterns.
Research Collections
Browse by topic.
Publications are grouped by the problems they address, not by the order they were written.
Rules of Engagement for AI Agents
A mission-level operating contract for cyber agents: bounded scope, per-action authority, human approvals, stop conditions, and auditable execution.
Intended audienceSecurity teams, cyber operators, AI platform engineers, and governance leaders defining authority for agents in operational workflows.
The Evening Star AI Operating Principles
The operating standard behind the work: respect the operator, show uncertainty, connect evidence to action, and keep automation bounded.
Intended audienceFounders, technical leaders, AI builders, operators, and collaborators evaluating the institute's operating philosophy.
The Evening Star AI Engine
The reusable architecture behind the anomaly work: baselines, detector ensembles, drift, attribution, confidence, and action boundaries.
Intended audienceAI engineers, security architects, platform builders, and technical executives evaluating reusable operational-intelligence systems.
Purple Radar: AI-Driven Vulnerability Intelligence
A model for sorting vulnerability noise into exposure, exploitability, asset context, risk rationale, and remediation priority.
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 gateway pattern for hostile prompts, jailbreak pressure, indirect attacks, risky tool use, sensitive data exposure, and unsafe model output.
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 paper on OHLCV features, volatility expansion, regime shifts, anomaly markers, and decision signals in a noisy domain.
Intended audienceMarket operators, product builders, decision-system designers, and analysts studying noisy, fast-moving environments.
Practical Evals for Agentic Systems
A test stack for agent task success, side effects, tool behavior, policy violations, recovery, escalation quality, cost, and traceability.
Intended audienceAI engineers, platform teams, model-risk leaders, security reviewers, and operators responsible for agent deployment.
MCP Security and Tool-Space Governance
A security model for MCP-style tool ecosystems through tool identity, namespaces, least privilege, policy mediation, execution isolation, and verification.
Intended audienceAI platform teams, security architects, MCP adopters, connector owners, and governance stakeholders.
The Operational Intelligence Layer
The category paper for the layer between raw signals and action: context ingestion, detection, reasoning, confidence, policy alignment, explanation, and next-step design.
Intended audienceExecutives, platform architects, applied AI teams, security leaders, and operators working in sensitive operational domains.
The Evening Star AI Governance Stack
An architecture for connecting policy intent to evals, runtime controls, audit logs, approval paths, incident response, and system improvement.
Intended audienceCTOs, CISOs, platform leads, staff engineers, and teams building governed AI systems.
Governing AI for Better City Operations
A municipal AI governance model for accountability, innovation, public trust, procurement discipline, and risk-tiered oversight.
Intended audienceMayors, city managers, CIOs, procurement leaders, legal teams, civil-rights leaders, and municipal executives governing AI adoption.
Governing AI for Better City Operations: Executive Brief
An executive brief for municipal leaders on why AI governance is an enterprise management discipline, not just a technology project.
Intended audienceMayors, city managers, department leaders, and executive teams evaluating municipal AI governance priorities.
The Case for Governed Agentic AI in Municipal Government
A public-sector operating model for governed agentic AI in municipal workflows, service delivery, procurement, finance, records, and resident operations.
Intended audienceCity managers, CIOs, department heads, procurement leaders, and elected officials navigating governed AI-enabled operations.
The Case for Governed Agentic AI in Municipal Government: Executive Brief
An executive brief on how governed agentic AI can reduce administrative friction while preserving human accountability in municipal government.
Intended audienceMunicipal executives, CIOs, department heads, procurement leaders, and elected officials evaluating operational AI pilots.
Red Teaming Agentic AI Systems
A system-level red-team model for prompt injection, context poisoning, unsafe tool use, permission bypass, goal drift, data leakage, and cross-agent propagation.
Intended audienceSecurity teams, red teams, AI safety teams, agent platform engineers, and governance reviewers.
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