November 29, 2025
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Most organizations still think of AI risk as model risk. They focus on prompt injection, bias or training data leakage. But today, that’s just the visible tip of a much larger ecosystem. Every AI-powered product, whether it’s a chat assistant, copilot or automation agent, depends on a constantly evolving network of external systems. APIs get updated, vector databases reindex, connectors change authentication flows and retrieval models silently retrain.
Each of these moving parts introduces a new variable in your risk surface and together, they form an unmonitored, unversioned and often misunderstood “shadow supply chain”.
A modern AI stack is rarely a single model sitting in an isolated container. It’s a choreography of moving parts:
✔ A model hosted by one provider (say, OpenAI or Anthropic).
✔ A retrieval index running on a vector database like Pinecone.
✔ Connectors to internal systems like CRMs, ticketing tools, analytics warehouses.
✔ API endpoints for actions: “send email,” “generate report,” “create task”
Every one of those dependencies changes independently. APIs update. Vendors patch endpoints. Plugins evolve. Embedding models refresh. And the agent which orchestrates it all, continues to make assumptions that may no longer be true. That’s not a bug. That’s the design. And that design is what creates your shadow supply chain.
The software industry already learned this lesson once. Over the past decade, we saw how open-source dependencies and CI/CD pipelines created “invisible” supply chains that attackers could exploit. SolarWinds, Log4j and Codecov weren’t direct hacks – they were transitive risks.
AI systems are repeating that history but faster. Where the old software supply chain revolved around code, the AI supply chain revolves around actions. Each time an AI agent decides to call a function, invoke a connector or pull from a retrieval layer, it’s effectively adding a new supplier to its process. The difference is that this chain isn’t static, it mutates at runtime, driven by natural language inputs.
When you ask a model to “summarize Q3 customer churn drivers,” you are not just prompting a model. You’re triggering a small, temporary economy of data requests, queries and tool actions that may cross multiple services. It’s a distributed supply chain. Invisible, adaptive and dangerously unmonitored.
Let’s look at a real-world scenario. Imagine a financial services firm deploying an internal “research assistant” for analysts. The system uses:
✔ An LLM agent for reasoning
✔ A retrieval pipeline connected to company research PDFs
✔ A plugin that can fetch stock data from a market API and
✔ A Slack connector for real-time discussion summaries.
It’s a simple architecture, until something goes wrong.
Last month, the market API updated its schema, returning user_email fields inside the payload instead of masking them. The agent, unaware, ingested that data into its working context. A retrieval query later surfaced those emails in a Slack summary. No malicious prompt. No jailbreak. Just normal behavior amplified by invisible dependencies. This is what makes AI risk unique, it’s not always about attacks but about drift.
Each small change can ripple through the system because the AI doesn’t just read data, it composes behavior with it.
Traditional security practices rely on visibility and boundaries: you know what your code does, what it touches and who can access it. But an AI system has a moving perimeter. It may make different calls depending on input phrasing, user role or the state of its memory. That means your security policies can’t just wrap the system. They need to follow its decisions.
Today, most organizations adopting AI can’t answer basic questions like:
✔ Which APIs did our AI system call last week?
✔ What data did it retrieve from which source?
✔ Did those systems change versions or permissions recently?
Without this visibility, every AI deployment becomes a potential black box in your infrastructure, one that interacts with sensitive systems through trust assumptions, not enforcement.
The rise of “Agentic AI” makes this even more complex. When models can plan, reason and execute multi-step tasks, the number of tool calls grows exponentially. Each call may introduce new dependencies, many of them temporary and unsupervised. For example, an autonomous agent might:
✔ Retrieve sales data from Snowflake.
✔ Call a forecasting API hosted externally.
✔ Write results to a Google Sheet shared with the team.
✔ Generate an email draft and send it via a mail API.
Every one of those steps crosses a trust boundary. A single unvalidated output could send sensitive data to the wrong place. Worse, the model’s reasoning might construct new API parameters on the fly. Meaning you can’t whitelist every possible path ahead of time.
Most current security responses focus on blocking unsafe prompts, blocking certain data patterns, blocking output keywords. That helps at the model level, but it’s reactive and shallow. The more effective strategy is tracing trust. AI security should start from a simple principle: every action an agent takes must be explainable, verifiable and traceable. Instead of trying to stop every bad thing the model might say, trace the lineage of what it does. That means building:
✔ Execution logs that capture every API call, retrieval, and output.
✔ Version fingerprints for external systems.
✔ Dependency graphs that show how one action leads to another.
✔ Automated tests that detect drift in tool behaviors or permissions.
If a model starts calling an endpoint it never used before, you should know. If a retrieval layer begins returning data from a new domain, you should know. It’s not about freezing the system but about making the invisible visible.
In traditional software, we track code provenance, we know where every dependency came from and who published it. In AI, we need the equivalent for actions: action provenance. Action provenance means that for every agentic step, you can trace its origin, context and effects. It’s the foundation for building a “chain of trust” across an AI’s reasoning graph.
This isn’t just theory. Early adopters in fintech and healthcare are already experimenting with AI observability layers that record tool use, API calls and data lineage. Some teams are using graph databases to visualize these agent workflows and automatically detect anomalies, like a connector being called outside its expected pattern. The goal isn’t to punish the model for exploring but to give security teams an audit trail they can actually reason about.
The phrase “shadow IT” once referred to teams running unsanctioned software outside corporate oversight. Today, “shadow AI” extends that concept into behavior, a network of invisible dependencies quietly powering your systems. You can’t eliminate that complexity. But you can illuminate it.
Start by mapping your AI’s external dependencies: every API, connector and data source. Instrument your agent frameworks to log tool actions. Treat retrieval layers like code – version them, test them and review access scopes. Over time, that transparency will evolve into something more powerful: a genuine AI supply chain you can understand, monitor and trust. Because your AI stack already has one, whether you have seen it or not.
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