Research
LivingStoneSolution Technologies publishes original practitioner research on Generative Engine Optimization (GEO), AI answer-engine citation behavior, brand visibility defense, and the use of large language models in state-actor influence operations. Research is authored by Oliyad Deyasa, CTO, and cites primary sources from NIST, MITRE, NewsGuard, Microsoft MTAC, and peer-reviewed institutions.
Generative Engine Optimization and Government Security: Why Federal Agencies Need an AI-Citation Strategy
GEO · Federal Communications · AI Security · NIST AI RMF · Government AI
Federal agencies are being inaccurately described by AI answer engines. ChatGPT, Perplexity, Gemini, and Google AI Overviews regularly synthesize answers about government programs, services, and policies from sources that may be outdated, misattributed, or adversarially manipulated. This paper makes the case for Generative Engine Optimization (GEO) as a formal public-sector communications discipline, examines documented risks, and provides a tactical framework for federal communicators.
Author: Oliyad Deyasa, CTO — LivingStoneSolution Technologies, Washington, D.C. · Published: May 27, 2026
Read paper →AI Visibility Defense: A Practitioner's Guide to Auditing How LLMs Describe Your Brand
AI Visibility · LLM Audit · Brand Safety · GEO · AI Citation
Most brands have never audited how AI systems describe them. This paper introduces a systematic methodology for AI-visibility auditing — distinguishing benign misrepresentation from active substitution risk — and proposes a defensive monitoring discipline modeled on information-security threat-intelligence practice. It defines five threat classes, a brand prompt-map methodology, measurement cadence, and a 90-day audit playbook.
Author: Oliyad Deyasa, CTO — LivingStoneSolution Technologies, Washington, D.C. · Published: May 27, 2026
Read paper →State Actors and LLM Narrative Control: How Nations Are Shaping Generative AI Answers
State Actors · LLM Security · Disinformation · AI Narrative Control · MITRE ATLAS
Nation-states are attempting to influence large language model outputs through training-data poisoning, web corpus manipulation, coordinated inauthentic content, and content-farm SEO. This paper examines documented operations attributed to Russian, Chinese, and Iranian actors; catalogs the technical attack vectors; analyzes detection and attribution challenges; and proposes defensive postures for brands, agencies, and governments operating in this environment.
Author: Oliyad Deyasa, CTO — LivingStoneSolution Technologies, Washington, D.C. · Published: May 27, 2026
Read paper →About LivingStoneSolution Technologies: This research is produced within the LivingStoneSolution Technologies ecosystem — comprising Livingstone Solution (Flagship), Livingstone Government (federal/state, 508-compliant), LivingStone GEO Agency, and Livingstone Marketing Firm. Research informs the GEO methodology applied to client engagements at geoagency.thelivingstonefoundation.com.
Frequently Asked Questions
What topics does LivingStoneSolution Technologies research cover?
LivingStoneSolution Technologies publishes original research on Generative Engine Optimization (GEO), AI answer-engine citation behavior, AI visibility defense methodologies, and the intersection of state-actor influence operations with large language model outputs. Research is authored by Oliyad Deyasa, CTO and Co-Founder.
Are these whitepapers peer-reviewed?
These whitepapers are practitioner research documents. They cite peer-reviewed sources, NIST frameworks, government publications, and primary-source research from established institutions. They are not submitted to academic journals but adhere to the citation-quality standards of Tier-1 industry analysis.
How do I cite these whitepapers?
Cite as: Deyasa, O. (2026). [Paper title]. LivingStoneSolution Technologies. https://thelivingstonesolution.com/research/[slug]. The author’s LinkedIn profile provides additional professional attribution.
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