The Emerging Threat of Shadow Operations

The cybersecurity landscape is evolving beyond data leakage concerns with LLMs. While 52% of security leaders cite sensitive data egress as their primary worry (according to SandboxAQ’s 2025 AI Security Benchmark Report), the real risk lies in the uncontrolled deployment of autonomous agents—what I call shadow operations.

These are non-supervised AI entities that execute logic, integrate with systems via APIs, and modify states without security oversight. Organizations have already widely adopted AI across business units, often using managed services or building their own agents, yet many lack visibility into what these agents can access or do.

The Rise of Agentic Frameworks

The rapid adoption of agentic AI frameworks like OpenClaw and Moltbot enables developers to automate tasks with minimal friction. While this accelerates innovation, it often bypasses secure-by-design principles.

A typical shadow ops scenario involves a developer using an agent to automate a complex workflow (like ETL or cloud deployment) and granting it high-privilege API keys—for example, AWS AdministratorAccess or GitHub Personal Access Tokens with full repository scope. The result is an autonomous entity running in a cloud function with broad access, invisible to traditional security tools.

Why Current Security Stacks Fall Short

Our existing security solutions are not designed for agentic environments:

  • Data Loss Prevention (DLP) and Identity and Access Management (IAM) often miss ephemeral AI identities
  • Cloud Security Posture Management (CSPM) tools only see legitimate servers running processes, not the unvetted AI logic calling external resources
  • Monitoring typically starts after deployment, missing the point of risk introduction—which occurs at code commit (pull requests)

The supply chain complexity also expands with agents, including every model, plugin, and external tool they can access. This was illustrated by recent incidents like OpenAI’s exposure through its analytics vendor, Mixpanel.

Towards AI Governance

Security teams need a unified inventory that maps which agents use which models, run on which hosts, and access what resources—a concept similar to an AI Bill of Materials (AI BOM).

While complete training data lineage may not always be available (particularly for fine-tuned or internal models), organizations should focus on:

  • Identifying managed third-party model calls
  • Discovering self-hosted models within repositories and cloud workloads
  • Establishing a baseline inventory before governance can be enforced

By shifting our security view from runtime to code commit, we can address the emerging risks of shadow AI operations.