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AI/ML Security Posture

Composite score measuring how well AI systems, LLM integrations, and ML pipelines are secured against adversarial threats and data poisoning

Budget Domain: AI Security
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Industry Benchmark

62%

+18.2% from previous period

Industry average: 54%

Calculation Method

Weighted average across five control categories: model access controls, training data integrity, prompt injection defenses, AI output monitoring, and AI vendor risk — each scored 0–100%

Significance

AI adoption has outpaced AI security. As LLMs handle sensitive data and business decisions, securing the AI layer is now a board-level cybersecurity requirement, not just an R&D concern.

What is AI/ML Security Posture?

AI Security Posture measures how comprehensively an organization secures its artificial intelligence assets — including internal ML models, third-party LLM APIs, AI-powered SaaS tools, and the data pipelines that feed them. It draws from OWASP LLM Top 10, NIST AI RMF, and MITRE ATLAS frameworks.

Key threat categories

  • Prompt injection — manipulating LLM behavior via crafted inputs
  • Training data poisoning — corrupting model outputs at the data layer
  • Model extraction — reverse-engineering proprietary models via API queries
  • Insecure outputs — LLMs generating code, SQL, or actions without guardrails
  • Shadow AI — employees using unsanctioned AI tools with company data

Why it matters in 2026

Rapid exposure growth: 78% of enterprises now use at least one LLM in production workflows (Gartner 2025), most without formal AI security controls.

Regulatory pressure: EU AI Act compliance requires risk classification and control documentation for high-risk AI systems deployed in the EU.

Data exfiltration vector: Prompt injection attacks have been used to extract confidential data from internal AI assistants with access to sensitive systems.

Maturity stages

  • 0–40%: No AI inventory, no controls, shadow AI rampant
  • 41–60%: AI inventory exists, basic access controls, limited monitoring
  • 61–80%: Prompt guardrails deployed, output logging, AI vendor assessments
  • 81–100%: NIST AI RMF aligned, red-teaming cadence, automated AI policy enforcement