AEGIS Research advances the safety, security, compliance, and operational reliability required for real-world deployment of LLMs, agents, multimodal systems, VLA pipelines, robotics, and next-generation intelligent systems.
We focus on the full spectrum of deployment risk — hallucinations, privacy leakage, security vulnerabilities, policy violations, and operational failure — developing practical technologies and frameworks for trustworthy AI adoption across industries.
10+
Research Areas
6
Publication Types
LLM
Guardrail Technology
2026
Established
AEGIS Research studies the real problems that emerge when LLMs and advanced AI systems move from demos into production.
Our work is centered on making AI verifiable, controllable, secure, and compliant in practical deployment settings. We cover not only hallucination reduction, but also guardrails, privacy protection, security engineering, policy enforcement, regulatory readiness, and response verification.
We extend our research to agent safety, multimodal risk control, VLA and robotics safety, and future AI deployment challenges. Our goal is to ensure AI systems operate safely within legal, regulatory, and operational boundaries across all deployment contexts.
Our research spans the critical domains required for trustworthy AI deployment in real-world environments.
Reducing unsupported responses and improving evidence alignment for trustworthy AI outputs.
Designing layered control systems that help prevent unsafe, non-compliant, or high-risk model behavior.
Addressing prompt attacks, misuse scenarios, privacy risk, and operational vulnerabilities in deployed AI systems.
Building methods for privacy-aware AI use, policy enforcement, and alignment with regulatory requirements.
Studying decision safety, action boundaries, escalation logic, and controllability in agentic AI systems.
Extending AI safety research into vision-language-action systems, embodied AI, and robotic environments.
Exploring future safety and governance implications of AI systems connected to emerging compute paradigms.
AI adoption is accelerating across every sector. But real deployment requires far more than model performance.
Whether AI outputs are supported by evidence, not just confident-sounding.
Whether responses comply with organizational policy and regulatory requirements.
Whether private and sensitive data is properly protected throughout AI operations.
Whether agent behavior remains controllable and operates within safe boundaries.
Whether systems operate within legal, regulatory, and operational boundaries.
AEGIS Research exists to address these questions through rigorous, practical, and deployment-oriented research. Our goal is not research for its own sake — we turn research into deployable technologies, evaluation protocols, operational standards, and product architectures.
Explore our latest papers, technical reports, benchmark studies, and whitepapers on trustworthy AI deployment.
Systematic review of 24 sources triangulating the expectation-reality gap in enterprise AI agent adoption across 8 axes, identifying governance as the largest gap with Korea-specific structural insights
Systematic review of 28 sources analyzing the expectation-reality gap in AI agent adoption for education across 6 axes, identifying academic integrity and equity as the largest gaps with lessons from Korea's AI textbook policy
Adaptive 4-bit quantization via Hadamard rotation and Lloyd-Max codebook achieving 8× communication reduction and 4× memory savings, validated to 355M parameters with GPU-accelerated Triton kernels
Economic analysis of TurboQuant's 8× communication compression and 4× memory savings on LLM training costs — projecting $240K–$1.2M savings for 70B model training and $8M infrastructure savings per 1,000-GPU cluster
Comprehensive benchmarking of 8 commercial LLMs across 7 adversarial algorithms evaluating layered guardrail effectiveness for high-risk enterprise deployments
An integrated safety diagnostics framework revealing all 8 tested LLMs are vulnerable with only 38.1% baseline defense rate across 112 evaluations
A comprehensive whitepaper covering governance, security, regulatory compliance, and operational readiness for safe enterprise LLM deployments
A lightweight multilingual classifier for real-time AI prompt security threat detection in browser extension environments
A four-layer hallucination defense framework for Korean financial services achieving >=97% detection rate, >=98% RAG accuracy, and <=200ms p95 latency
A four-layer pipeline architecture reducing LLM hallucination rates below 3% for mission-critical enterprise domains
We publish multiple forms of research to support different audiences and use cases.
Technical depth and methodological contribution
Applied implementation insights and practical guidance
Comparative evaluation and measurement
Strategic and operational guidance for decision-makers
Real-world deployment lessons and applied outcomes
Concise summaries for partners and stakeholders
Real-world relevance over abstract claims
Verification, control, and accountability over unchecked autonomy
Practical deployment readiness over isolated benchmark performance
Evidence, transparency, and limitations over exaggerated marketing
Research that connects to technology, operations, policy, and productization
We collaborate with enterprises, institutions, researchers, and public-sector stakeholders who need safer and more trustworthy AI deployment.
Whether you are evaluating LLM adoption, developing guardrail systems, studying AI regulation, or preparing next-generation AI infrastructure, AEGIS Research is designed to support practical progress.