Enterprise AIr/artificial

Anyone else feel like AI security is being figured out in production right now?

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ai-security-gapsshadow-ai-adoptionprompt-injection-attacksai-agent-permissionssecurity-frameworks-emerging

“Enterprises are averaging 300+ unsanctioned AI apps, and in many cases AI security isn't even owned by security teams”

Key takeaways

  • AI security is being figured out in production with enterprises running 300+ unsanctioned AI apps and most lacking dedicated AI security teams
  • Attack patterns mirror early-stage tech adoption: prompt injection, over-permissioned agents, and shadow IT rather than sophisticated exploits
  • Traditional security knowledge transfers incompletely - prompt injection ≠ SQL injection, agent permissions ≠ API auth - creating expertise gap despite emerging frameworks (OWASP, MITRE ATLAS, NIST)
  • AI is accelerating both sides: making it easier for attackers to find weaknesses while defenders struggle with ownership and skill gaps
  • Security frameworks exist (OWASP LLM/Agentic Top 10, MITRE ATLAS, NIST AI RMF) but practical implementation expertise is scarce

Why this matters for operators: Companies deploying AI tools without security governance; gap between AI adoption speed and security maturity

I cover AI×GTM intelligence like this every Wednesday.

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This analysis was produced using the STEEPWORKS system — the same agents, skills, and knowledge architecture available in the GrowthOS package.