Responsibilities:
- Build and lead a first-of-its-kind defensive program focused on countering autonomous systems and adversarial AI, including its charter, operating model, and technical roadmap.
- Mentor a team of analysts and engineers across reverse engineering, AI-assisted analysis, building depth in a scarce and fast-moving skill set.
- Identify, test, and validate detective, preventive, and compensating controls built to hold up against AI-driven and autonomous attack techniques, and measure how well they perform under realistic adversary conditions.
- Design and run deception and canary infrastructure. Identify, test, and validate deceptive controls, then seed, tune, and maintain canary assets that disrupt, misdirect, and neutralize autonomous adversaries and agentic attack tooling.
- Stand up and maintain a dedicated lab environment to simulate adversarial AI and autonomous attack scenarios and continuously stress-test control effectiveness.
- Recover, triage, and deconstruct malicious binaries, scripts, and payloads through static and dynamic reverse engineering to determine capability, intent, attribution signals, and autonomous behavior patterns.
- Build AI-assisted pipelines that automate triage and intelligence extraction at volume, and put that intelligence to work across detection engineering, threat hunting, incident response, and threat intelligence functions.
- Turn high-value intelligence into automated detection and response workflows that identify, intercept, and disrupt machine-speed attacks before they reach their objective.
- Map and predict the attack paths autonomous systems and adversarial AI are most likely to take, and use that insight to prioritize where defensive investment goes.
- Lead AI-enabled breach investigations, including forensic reconstruction of events, root cause analysis, and timeline development across complex, fast-moving intrusions.
- Work with red team, cloud security, AI/ML engineering, IAM, and enterprise architecture to build defensive requirements into AI adoption at the design stage rather than retrofitting them later.
- Define KPIs for program maturity, control coverage, and mean time to disrupt, and brief senior leadership and risk committees on the threat landscape and the organization's readiness against it.
- Map program outcomes to NIST AI RMF, MITRE ATT&CK and ATLAS, OWASP LLM Top 10, and evolving AI governance and regulatory requirements.
Qualifications:
- Minimum of five years related work experience, with three years of experience in red teaming, adversary emulation, or penetration testing.
- Undergraduate degree in a related field or the equivalent combination of training and experience
- Deep malware reverse engineering expertise (x86/x64, .NET, scripting); hands-on incident response and forensic reconstruction; proficiency in Python and automation tooling; working knowledge of LLM/agentic architectures and their attack surfaces; experience building detection or deception capabilities from scratch.
Preferred:
- Prior threat research or nation-state adversary tracking experience; familiarity with MITRE ATT&CK and ATLAS; experience applying ML to security analytics; published research or conference presentations; GREM, GCFA, OSCP, or equivalent.
Special Factors
Sponsorship
Vanguard is not offering visa sponsorship for this position.
