# OWASP's New Blueprint: The AI Security Skill Gap

> OWASP's new AI security blueprint names the team skill gap as a core risk: buy skills before tools if you want models that survive production.

**URL:** https://www.ciptadusa.com/blog/owasp-ai-security-blueprint-skills-20260822  
**Type:** blog  
**Author:** PT Cipta Dua Saudara  
**Category:** Application Security  
**Published:** 2026-08-22  
**Cover:** https://cdn-uagents.enitip.com/uploads/blog/2026-08/daily-appsec-20260822-014554.jpg  

## Article

When large language models move from prototype to production, security is no longer an afterthought — it is a prerequisite for release. OWASP, the community best known for its Top 10 web lists, has just published a new AI-focused security blueprint, and its headline concern lands on something that technical checklists often miss: the security skill gap itself. Dark Reading's coverage makes the point plainly — before worrying about prompt injection or model poisoning, organizations should ask who on their team is actually equipped to handle those threats.

## Summary
OWASP's new AI security blueprint names the team skill gap as a core risk. AI application security fails less from missing tools than from too few practitioners who understand models, data, and pipelines.

## Background
AI applications present a different attack surface than conventional software: the prompt layer, the retrieval flow, the model itself, and integrations with external tools. OWASP has long mapped the underlying technical threats, but this blueprint shifts focus to the organizational dimension. The risk is not only the type of attack but whether the security team has the skills to detect and respond. Without that expertise, even the best technical controls — input filtering, sandboxing, monitoring — amount to window dressing over vulnerabilities nobody really understands.

## Approach
The blueprint proposes building capability in three layers. First, foundational skills: security teams must understand how large language models work, including why prompt-based attacks cannot be solved with keyword blocklists. Second, engineering skills: the ability to audit a pipeline from data collection to fine-tuning, aligning security controls with the MLOps flow already in place. Third, response skills: playbooks for incidents involving models, which differ from ordinary application incidents because the attack vector can arrive via seemingly legitimate user input. All of this demands a shared operating model between security and data-science teams, not siloed functions.

## Implications
The lesson for organizations adopting AI quickly is that model security cannot simply be outsourced. Buying an AI security tool does not replace the need to understand how your model is used and attacked. In Indonesia, where enterprise AI adoption is climbing fast, investing in AI security skills is more urgent than adding to a security-product list. Teams that build AI security competence during design will avoid costly rework once a model is live. The OWASP blueprint offers a route map: build skills before buying tools, and treat AI security as a shared responsibility between engineering and security.

## References
- Dark Reading: [OWASP Flags Top AI Skill Risks in New Security Blueprint](https://www.darkreading.com/application-security/owasp-flags-top-ai-skill-risks-security-blueprint)
- [OWASP Top 10 for LLM Applications](https://owasp.org/www-project-top-10-for-large-language-model-applications/)
- [NIST AI Risk Management Framework](https://www.nist.gov/itl/ai-risk-management-framework)

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*Markdown version of https://www.ciptadusa.com/blog/owasp-ai-security-blueprint-skills-20260822 — generated for AI agents and LLM crawlers.*