AI Security 101: What Adopting AI Actually Means for Your Attack Surface

AI Adoption Is Moving Faster Than AI Security

Most organizations adopting AI — whether that's using AWS Bedrock, OpenAI's or Anthropic's APIs, or building internal AI agents — are moving fast on capability and slow on security. That gap is where the risk lives. This isn't a theoretical future problem; it's a current one for any team that has already shipped an AI feature or given an internal team API access to a model.

AI Doesn't Have One Attack Surface — It Has Several

"AI security" is often talked about as if it's one thing. In practice, adopting AI expands your attack surface across several distinct areas, each with different failure modes.

1. The Application Layer

If you've built an application on top of an LLM — a chatbot, an internal tool, a customer-facing agent — you've introduced new categories of vulnerability that don't map cleanly onto traditional web app security:

• Prompt injection — malicious input designed to override the system's intended instructions, either directly (a user typing an attack into a chat box) or indirectly (malicious content embedded in a document or webpage the model is asked to process)
• Context poisoning — manipulating the data or context an AI system relies on, so its outputs are subtly wrong or exploitable
• Data leakage — the model exposing information it shouldn't, whether from its training, its context window, or connected systems
• Insecure tool use — if the model can call functions or tools (search a database, send an email, execute code), those tool calls need the same access controls and validation as any other privileged action

2. Agent Permissions

Agentic AI — systems that can take multi-step actions rather than just generate text — raises the stakes considerably. An agent with excessive permissions doesn't just produce a bad answer; it can take a bad action. Common gaps:

• Agents granted broader access than the task requires ("just give it admin access so it works")
• No human-in-the-loop checkpoint for high-impact or irreversible actions
• Insufficient logging of what an agent actually did, making incidents hard to investigate

3. Data and RAG Pipelines

Retrieval-Augmented Generation (RAG) systems pull from vector databases and internal knowledge stores to ground model responses. That pipeline introduces its own risks:

• Vector databases often replicate access-control gaps from source systems — if a document shouldn't be visible to a user, it shouldn't be retrievable by a RAG system on their behalf either
• Poisoned or manipulated source documents can influence model outputs
• Sensitive data can end up embedded in vector stores without the same protections as the original source

4. Vendor and Model Risk

Every AI vendor — whether it's a foundation model provider or a SaaS tool with an "AI feature" bolted on — is a new third party with access to some slice of your data and workflows. Standard vendor risk questions apply, plus AI-specific ones: What data is used for model training? Is there data residency or retention control? What happens to prompts and outputs?

5. Governance and Human Oversight

The organizational layer matters as much as the technical one. Without governance:

• Employees adopt AI tools informally, outside any review process ("shadow AI")
• There's no clear policy on what data can be sent to which AI systems
• No one owns AI risk decisions, similar to the "who owns security" gap many companies have before hiring security leadership

A Practical Starting Checklist

You don't need a perfect AI security program before you can start. A reasonable first pass looks like:

1. Inventory — what AI applications, vendors, and internal tools are actually in use today? (Often more than leadership expects.)

2. Classify by risk — which of those touch sensitive data or take real-world actions, versus low-risk internal productivity use?

3. Set a usage policy — what's approved, what requires review, what's prohibited — and make sure people actually know it exists

4. Review the highest-risk systems first — prioritize customer-facing AI applications and agents with tool access over internal chat assistants

5. Establish human oversight for high-impact actions — no agent should be able to take an irreversible action without a checkpoint, at least until the system has a track record

This Isn't Just a "Someday" Problem

Organizations are often surprised, once they actually inventory it, how much AI is already running in production — often adopted by individual teams without a formal security review. The risk isn't hypothetical; it's already deployed. The question is whether it's been assessed.

Adopting AWS Bedrock, OpenAI, or Anthropic's models and want a clear-eyed view of what needs securing? Get an AI security assessment — a practical review of your AI applications, vendors, and governance gaps, not a theoretical framework exercise.