What Is AI Discovery in AI Development?
AI is reshaping how organizations build and deliver software. From customer-facing applications to internal agents that automate workflows, AI is being woven into the code we develop and deploy in the cloud. But as the pace of adoption accelerates, most organizations lack visibility into what exactly is inside the AI systems they are building.
AI discovery in this context means identifying and understanding the models, agents, system prompts, and dependencies that make up your AI applications and agents. It’s about shining a light on the systems you are directly developing.
In short AI discovery ensures you know what you’re putting into production before attackers, auditors, or customers find out the hard way.
Why AI Discovery in Development Matters for Risk and Security
The risk isn’t just that AI is everywhere, it’s that organizations often don’t know what’s included in the systems they’re developing. That lack of visibility creates openings for security failures and compliance gaps.
- Unknown Components = Hidden Risk: Many AI pipelines rely on open-source models, pretrained weights, or third-party data. Without discovery, organizations can’t see whether those components are trustworthy or vulnerable.
- Compliance & Governance: Regulations like the EU AI Act and NIST’s AI Risk Management Framework require organizations to understand the models they create. Without discovery, you can’t prove accountability.
- Assessment Readiness: Discovery is the prerequisite for evaluation. Once you know what models and agents exist, you can scan them for vulnerabilities or run automated red team exercises to identify weaknesses.
- Business Continuity: AI-driven apps often become mission-critical. A compromised model dependency or poisoned dataset can disrupt operations at scale.
The bottom line is that discovery is not simply about finding every AI product in your enterprise but about understanding the AI systems you build in development and cloud environments so you can secure them properly.
Best Practices for Effective AI Discovery in Cloud & Development Environments
Organizations leading in AI security treat discovery as a continuous discipline. Done well, discovery doesn’t just tell you what exists but highlights what needs deeper evaluation and protection.
- Build a Centralized AI Inventory
- Catalog the models, datasets, and pipelines being developed in your cloud and dev environments.
- Capture details like ownership, purpose, and dependencies.
- Visibility here ensures you know which assets must undergo assessment.
- Catalog the models, datasets, and pipelines being developed in your cloud and dev environments.
- Engage Cross-Functional Stakeholders
- Collaborate with data science, engineering, compliance, and security teams to surface “shadow AI” projects.
- Encourage openness by framing discovery as a means to enable innovation safely, rather than restricting it.
- Collaborate with data science, engineering, compliance, and security teams to surface “shadow AI” projects.
- Automate Where Possible
- Use tooling to scan repositories, pipelines, and environments for models and AI-specific workloads.
- Automated discovery enables automated security assessments to follow.
- Use tooling to scan repositories, pipelines, and environments for models and AI-specific workloads.
- Classify Risk and Sensitivity
- Tag assets by criticality, sensitivity, and business impact.
- High-risk assets, such as those tied to customer interactions or financial decision-making, should be prioritized for model scanning and automated red teaming.
- Tag assets by criticality, sensitivity, and business impact.
- Integrate with Broader Risk Management
- Feed discovery insights into vulnerability management, compliance reporting, and incident response.
- Traditional security tools stop at applications and infrastructure. AI discovery ensures that the AI layer in your development environment is also accounted for and evaluated.
- Feed discovery insights into vulnerability management, compliance reporting, and incident response.
The Path Forward: From Discovery to Assessment
AI discovery in development environments is not about finding every AI-enabled tool in your organization, it’s about knowing what’s inside the AI applications and agents you build. Without this visibility, you can’t effectively assess or secure them.
At HiddenLayer, we believe security for AI starts with discovery, but it doesn’t stop there. Once you know what you’ve built, the next step is to assess it with AI-native tools, scanning models for vulnerabilities and red teaming agents to expose weaknesses before adversaries do.
Discovery is the foundation and assessment is the safeguard. Together, they are the path to secure AI.
