Focus Areas
Four interconnected domains covering the attack surface of modern AI systems — from model training to deployment, from evasion to exploitation.
Focus Area
Modern security operations centers generate millions of log events, alerts, and telemetry data points daily — far beyond human analyst capacity. This research area explores how large language models, vector search, and RAG pipelines can be applied to automate threat detection, triage, and incident summarization at scale.
The focus is on practical, deployable tooling: systems that fit into real SOC workflows, augment analyst judgment, and surface the high-fidelity signals buried in noise.
The Scale Problem
The average enterprise SIEM generates 10,000+ alerts per day. Analysts can only triage a fraction. AI-assisted defense isn't a nice-to-have — it's a structural necessity. Companies that get this right will have a decisive security advantage.
Research Status
ActiveKey Concepts
Target Roles
Focus Area
Shipping a model to production without security controls is the AI equivalent of deploying an unauthenticated web server. This research area is about building the security primitives that should be standard in every ML pipeline: model scanning, dependency pinning, secrets management, provenance tracking, and supply-chain integrity.
The goal is to produce a reusable, production-grade Secure MLOps Template that any team can fork and deploy — engineering security culture into the workflow itself.
The Supply Chain Gap
Model weights downloaded from public hubs have no mandatory verification. A poisoned model checkpoint can persist undetected through fine-tuning. Most ML teams have no SBOM for their models. This is a solved problem in software security — it just hasn't been ported to AI yet.
Research Status
PlannedKey Concepts
Target Roles
Focus Area
Adversarial ML sits at the intersection of security and deep learning: small, carefully crafted perturbations to inputs can fool state-of-the-art neural networks with high confidence. This research area covers the full attack surface — white-box attacks like FGSM, PGD, and C&W, black-box transfer attacks, and the defenses that actually hold up under pressure.
The goal is not just to implement attacks but to understand the geometry of feature spaces, evaluate robustness certifications, and produce reproducible experiments that cut through the hype in the literature.
High-Stakes Evasion
Adversarial examples have been demonstrated against autonomous vehicle perception, medical imaging classifiers, malware detectors, and facial recognition systems. When AI makes safety-critical decisions, adversarial robustness isn't academic — it's a product requirement.
Research Status
PlannedAttack Families
Target Roles
Focus Area
AI red teaming is the systematic adversarial testing of AI systems — going beyond functional QA to probe safety boundaries, alignment failures, and attack surfaces that only emerge under adversarial pressure. This research area builds the tooling and methodology to make AI red teaming rigorous, repeatable, and scalable.
The north star is the LLM Red Teaming Evaluation Suite: an automated framework that covers jailbreaks, safety bypasses, toxicity elicitation, PII extraction, and prompt leakage — producing structured reports that inform safety alignment work.
Safety Cannot Be Asserted — It Must Be Demonstrated
RLHF and Constitutional AI reduce but do not eliminate harmful outputs. Red teaming uncovers the cases that slip through safety training. Organizations like OpenAI, Anthropic, and Google DeepMind now run dedicated red teams before every major model release — and hire accordingly.
Research Status
PlannedAttack Vectors
Target Roles