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The State of AI Security Engineering · 2026

300,000 job postings. 15 market signals. One discipline.

Analyze 300K+ job postings to prove what companies really hire for. Benchmark your team, define the role, and ship AI security with evidence.

Trusted by 385+ respondents across security, hiring, and operations teams.

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300,000+

Job descriptions analyzed

↑ 50% vs 2025

385+

Survey respondents

Growing with every wave

15

Named findings

Market concepts, not buzzwords

9

Role archetypes

Mapped across hiring intent and maturity

26

Publication chapters

Across 3 volumes

99%

Editorial independence

No sponsor influence

Why this report matters right now

AI security is growing fast, but the discipline is still unmeasured

Hiring, governance, and product teams are operating on fragments of evidence. This report closes the gap with data-driven definitions, clear role benchmarks, and practical signals for the market.

Roles are undefined

Job titles say "AI security engineer" but mean wildly different things. The result is scope creep, skill mismatch, and hiring failures.

Skills are unclear

Teams don't know what to hire for. No taxonomy, no benchmarks, and vendors fill the vacuum with marketing language.

Teams are isolated

Every company believes it's unique. No peer comparison and no evidence of what works slows maturity and adoption.

See how we solved this

What we've built

One platform, four surfaces

Portfolio Proof

Case studies, assessments & research

A public-safe selection of engagements and research spanning AI security, product security, and applied engineering.

View full portfolio
The State of AI Security Engineering Report 2026
ResearchOpen case study

The State of AI Security Engineering Report 2026

A flagship research report turning AI security job-market noise into evidence about roles, skills, control gaps, hiring signals, and the emerging AI security engineering discipline.

AI Security LLC2026Explore
The AI Security Engineering Field Guide
ProductOpen case study

The AI Security Engineering Field Guide

A compact, action-oriented field guide for AI security engineering practitioners working in fast-moving environments.

aisecurity.llc2026Explore
AI Product Security in the Age of Mythos
ProductOpen case study

AI Product Security in the Age of Mythos

A practical AI product-security framework for agentic systems, governance evidence, excessive agency, RAG authorization, and continuous threat modeling.

AI Security LLC2026Explore

Drag or use arrows

Flagship findings

The market is defining the discipline

All 24 findings

People · Services · Proof

Meet the team. Review the work. Engage directly.

Browse the full roster of consultants and advisors, explore service lanes with clear scope, and review case studies across 50+ portfolio engagements — all cross-linked so you can follow threads from any starting point.

Portrait of David Wolf
David Wolf

Senior AI Security Engineer who builds the operating model, controls, detection, and evidence layer for enterprise AI adoption.

AI Security EngineeringResearch & AnalysisTechnical Delivery & Architecture

David Wolf is a Senior AI Security Engineer who builds the operating model, controls, detection, and evidence layer for enterprise AI adoption. He brings 25+ years of security engineering and applied research expertise to the intersection of AI risk, data governance, and enterprise controls. He translates job-market signals, control evidence, and technical architecture into practical advisory work and productized security frameworks.

Services

AI security control-plane design and implementationAI security posture review and governance readinessResearch briefingArchitecture review

Featured work

Splunk Product Security Program BuildoutThe State of AI Security Engineering Report 2026AI Governance Controls with Garak, NeMo Guardrails, Presidio & PromptfooBrowser-Native Trust Boundary Security Model
AvailableArchitecture / research engagements: $25K-$50K
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FlagshipAvailable

architecture review

AI Product Security Architecture Review

Review an AI-enabled product or feature before it becomes an incident. We map trust boundaries, data flows, model/provider dependencies, authorization paths, abuse cases, logging gaps, and remediation priorities.

Outcome

7 deliverables

Best for

CISO, Head of Product Security, VP Engineering, AI Platform Lead

  • •AI feature threat model and trust-boundary map
  • •Model, provider, and data-flow risk review
  • •Authentication, authorization, and tenancy assessment
Duration: 3-6 weeksRate: Custom quote
Schedule discoverySee people fitView proof
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Publications

Long-form research and reference

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The State of AI Security Engineering Report 2026