AI Engineer · Cybersecurity Innovator · Gamification Pioneer
Clint Bodungen
At the intersection of AI, cybersecurity, and games — building with AI since 2013, long before the wave.
The arc
Cybersecurity is where I come from — innovation is what drives me.
For 30+ years I've done the same thing on repeat: bring an emerging technology into a field before it's obvious — first gamification, then AI. Find the frontier, and make it real.
The origin
Gamification of Cybersecurity
Co-created the world's first online, multiplayer, game-based cybersecurity simulation — making security training something you play, not sit through.
Since 2013
AI in Cybersecurity
Brought AI into security tooling and training years before the generative-AI wave — and published on generative AI for security while most of the field was still watching.
The convergence
AI + Gamification
Adversarial game AI, autonomous AI exercise facilitation, and AI-driven training — the two frontiers, fused.
Now
AI in Games & AI That Builds
AI in commercial video games — and AI systems that engineer software themselves. The domain keeps changing; the pattern doesn't.
Capabilities
An unusually broad AI toolkit
From classic and bio-inspired AI to modern LLM and agentic systems — techniques applied across games, enterprise platforms, and security research.
Agentic & LLM Systems
- Multi-Agent Orchestration
- Retrieval-Augmented Generation (RAG)
- Structured / Constrained Generation
- LLM-as-Judge & AI Evaluation
- Persistent AI Identity & Memory
Bio-Inspired & Classic AI
- Swarm Intelligence (Ant Colony Optimization)
- Evolutionary Computation & Genetic Algorithms
- Neural Networks & Neuroevolution
- Multi-Objective Optimization
Game AI
- Utility-Based Behavior Trees
- Adversarial Search (Minimax)
Data & Knowledge
- Security Knowledge Graphs (Neo4j)
- Vector Databases & Semantic Search
01 — Portfolio
Flagship work
Named products and research, plus a genericized enterprise engagement. Every AI claim traces to real, shipped systems.
Research · personal
Project DARWIN — Bio-Inspired Cyber-AI
Open research applying bio-inspired AI to cyber risk: virtual 'ant' swarms (Ant Colony Optimization) traverse a Neo4j attack graph of MITRE ATT&CK / D3FEND / CVSS to surface the most probable, high-impact attack paths, while genetic algorithms evolve adaptive defenses.
Enterprise · critical-infrastructure security client
AI Vulnerability-Management Platform
A security knowledge graph (Neo4j) fusing enterprise assets, vulnerabilities, MITRE ATT&CK / D3FEND, and CISA KEV exploit intelligence, with a multi-agent AI layer for natural-language querying and a four-layer AI asset-deduplication engine. Delivered under a fixed-price engagement.
Experience
AI résumé
Leading AI/ML engineering, pioneering AI in cybersecurity products, and self-directed AI research — a track record that predates the current AI wave by a decade.
Arcova
formerly MorganFranklin Cyber · Full-time · RemoteDirector, AI/ML Engineering
Aug 2025 – Present- Lead the AI/ML engineering team building production, agentic-AI applications across cybersecurity and GRC — including an AI-powered third-party risk-management platform (automated vendor tiering, evidence mapping from trust centers, OSINT/dark-web risk monitoring), an AI change-management intake system, and LLM-powered go-to-market and sales-intelligence tools.
- Architect multi-agent and LLM systems using retrieval-augmented generation (RAG), structured/constrained generation, tool-using research agents, and provider-abstracted multi-model integration (Anthropic, OpenAI, and local models).
- Established an AI-efficacy testing framework — multi-layer, including LLM-as-judge evaluation — as an engineering release gate that measures whether AI features actually perform, not just whether the code runs.
- Advise on the secure integration of agentic AI — human-in-the-loop controls, source provenance/confidence tracking, and deterministic fallbacks.
- Drive AI-native product strategy, reframing document-heavy GRC workflows around AI ingestion/inference layers that draft analysis from evidence with per-field provenance.
Director, Cybersecurity Innovation
Aug 2024 – Aug 2025- Led an innovation engagement delivering an AI-powered vulnerability-management platform for a critical-infrastructure security client — fusing enterprise assets, NVD vulnerabilities, MITRE ATT&CK / D3FEND, and CISA KEV exploit intelligence into a Neo4j security knowledge graph queryable in natural language.
- Designed a multi-agent AI layer (LangChain / LangGraph): natural-language-to-Cypher graph querying, RAG-based security chatbots, and a self-extending meta-agent that writes its own graph analytics from plain-English use cases.
- Built an exploit-aware, four-layer AI asset-deduplication engine (deterministic fingerprinting → semantic-embedding similarity → graph adjacency → LLM adjudication) that cut LLM cost ~70% via cheapest-method-first tiering.
ThreatGEN
Founder / Chairman / Head of Product Innovation · Full-timeFounder / Chairman / Head of Product Innovation
Jul 2017 – Present- Founded a funded cybersecurity startup closing the skills gap through gamification and AI-driven training — built on modern game engines, simulation technology, and Generative AI/LLMs.
- Co-creator of ThreatGEN® Red vs. Blue — the world's first online, multiplayer, game-based cybersecurity simulation — including its adversarial red-team/blue-team game AI: utility-based behavior trees with animation-curve utility scoring (plus an exploratory neural-network opponent), now re-engineered for the web in TypeScript.
- Creator of ThreatGEN AutoTableTop™ — an AI-powered incident-response tabletop-exercise platform driven by a multi-agent LLM system (an AI facilitator, scenario/timeline agents, and dynamic inject delivery) with structured outputs and real-time orchestration.
- Pioneering the applied use of Generative AI and LLMs across cybersecurity training, exercises, and real-world application — spanning game AI, autonomous exercise facilitation, and AI-assisted content generation.
Independent AI Research
OngoingFounder & Principal Researcher
Building with AI since 2013- Designed a persistent-identity and long-term-memory architecture for LLM agents (Layered Continuity Architecture) — vector-backed semantic recall with experiential-salience weighting and dream-cycle consolidation — enabling agents to keep identity and knowledge across sessions and across different underlying models.
- Built a substrate-neutral, multi-agent software-engineering framework that orchestrates ~20 specialized AI sub-agents under a codified SDLC, with a rapid-prototyping methodology and an adversarial verification loop.
- Demonstrated, via pre-registered blind-judged A/B experiments, that the harness lifts a lower-cost model into a frontier model's shipped-quality band — isolating the gap as engineering discipline, not raw capability.
- Pioneering bio-inspired AI for cybersecurity (Project DARWIN) — Ant Colony Optimization for attack-path discovery combined with genetic algorithms for adaptive defense.
02 — Research
Research & thought leadership
Self-directed work at the frontier of applied AI — bio-inspired methods, persistent agent identity, and the empirical study of what actually makes AI systems perform.
Bio-Inspired AI for Cyber Risk (Project DARWIN)
Swarm intelligence (Ant Colony Optimization) for attack-path discovery, fused with genetic algorithms and neuroevolution for adaptive defense — a multi-objective, nature-inspired approach to modeling attacker behavior and evolving mitigations.
03 — Applied impact
AI agents doing real work
Beyond experiments — the persistent-agent systems applied to autonomous security operations and high-stakes incident response.
04 — Authority
Speaking, media & publications
Published author, speaker, and educator on AI and cybersecurity.