Every surface
One cross-surface platform managed by your best security engineers. Comprehensive platform over point solutions.
In the agentic era, backlogs are growing geometrically, but remediation capacity has remained constant. We’re building Abundant Security Labs to flip scarcity to abundance.
We create AI to work for defenders: automatic remediation that scales protection to every organization, while keeping human judgement in command.
Meet Abundant Security Labs
The engine that powers our Exposure Remediation platform. The principles, science, and people behind how we’re making cyber attacks economically unviable – ensuring AI is a force that accelerates defenders.
The principles behind our work.
Resolution, not backlog
Always-on autonomous agents that drive findings to resolution. Less time triaging alerts. More long range, deep architectural hardening.
Science over hype
30+ years combined leading security programs. Science-backed AI grounded in real-world security operations—not marketing hype.
Humans AND robots
AI innovation is people, not just models. Human behavior drives how we build product, AND how we implement, onboard, and train users – to ensure harmony between human and machine.
Customer-led, really
Founded by researchers and practitioners, the platform is shaped by continuous feedback from security leaders solving today's operational challenges – evidenced by our roadmap.
Transparency over snake oil
Yes, up-leveling your team, security, ROI, speed, and shelter from the Cyber Apocalypse are important. But we’re honest and open about what we can and can’t deliver.
Pioneering the science of automated remediation.
Security harness
Purpose built for security orgs: deterministic prompt injection controls, least privilege identity scoping, cryptographic trust chains across agent boundaries, blast radius containment.
Knowledge graph
Security assets coupled with business context and the people that own them. Learned and validated from enterprise context.
Security agent workforce
Specialized agents: investigations, root-cause analysis, remediation, validation – working together on one job: the fix.
Purpose built models
High-throughput models purpose built to outpace triage volumes in an agentic world, and automate remediation via action-state prediction.
Earnest humans ensure we’re on the right side of AI history.
The researchers behind Abundant’s scientific rigor.
Hyrum Anderson
CEO / Co-Founder
- Chaired Microsoft’s AI Red Team governing board and architected its inaugural red-teaming of production AI systems.
- Co-founded CAMLIS, the Conference on Applied Machine Learning in Information Security.
- Appointed to the 2024 National Academies study on Cyber Hard Problems.
- Advisor to the US and UK governments on AI safety and security.
- 60+ peer-reviewed publications; co-author of Not With a Bug, But With a Sticker.
- Speaker at RSA, BlackHat, and DEFCON.
- PhD, University of Washington.
Google ScholarJosh Saxe
CTO / Co-Founder
- Led security for Meta’s large language models, defending them from application-level attacks.
- Former Chief Scientist at Sophos.
- Principal investigator on multiple DARPA programs at Invincea Labs.
- Led machine-learning security research at Applied Minds.
- Co-author of Malware Data Science; dozens of papers and patents on security AI.
- Speaker at DEFCON, BlackHat, and RSA.
Chris Monson
Member of Technical Staff
- Senior Security Architect and AI Trust & Safety Lead at Atlassian; formerly Engineering Manager and Senior ML Engineer at Meta.
- CTO at Data Machines Corp., directing DARPA research programs; earlier a Tech Lead at Google.
- Co-author of Practical Cryptography in Python (Apress) and author of Introduction to Programming for the Independent Student.
- Three US patents in intrusive-software and malware detection.
- 20+ peer-reviewed papers in machine learning and optimization (GECCO, CEC, IEEE SSCI, IJCNN).
- Lecturer in cloud-computing security at the Johns Hopkins Information Security Institute.
- PhD in Computer Science, BYU.
Malachi Jones
Member of Technical Staff
- Former Principal Cybersecurity AI/LLM Researcher and Manager at Microsoft, with four years leading initiatives across Microsoft Security AI and the Cloud Security Lab.
- Fine-tuned large language models for security tasks, built autonomous red-team agents, and developed binary-analysis and reverse-engineering capabilities for Security Copilot.
- Conducted advanced machine-learning and intermediate-representation research at The MITRE Corporation to automate reverse engineering, and designed an internal training course on the subject.
- Performed embedded-security research at Booz Allen Dark Labs and co-authored U.S. Patent 10,133,871.
- Earned a Ph.D. in Computer Engineering from the Georgia Institute of Technology.
- Served as an Adjunct Professor at the University of Maryland, College Park, teaching machine-learning applications for cybersecurity.
