Executive Insight: What I Tell Every AI Startup That Asks Me About Security & Audit Readiness
Updated: Oct 8
Last weekend, I sat down with two co-founders and their first employee. They were a fresh AI startup, with their product barely out of the oven. Yet, they were already fielding questions from prospective customers about security and audit! As part of my commitment to mentoring in various incubators, I’ve had this conversation multiple times. So, I decided to write it down.
The questions were the usual ones that arise when a startup begins selling to enterprises: What models are you using? Are your agents safe? Have you passed an audit? Here’s the framework I walked them through.
Start With the Product: Think in Layers
Every AI product, no matter how simple it appears, is built on three layers: Model, Agents, and Data. Get clarity on each one before a customer's security team asks you to.
The Model Layer
Which models, and why? Will you use open source or not? Will you host it yourself or use their API? What enterprise agreement do you have in place for data protection? There’s no universally “right” answer. However, a startup selling into regulated industries will face different model-approval bars than one selling to indie developers.
Know your goals for LLM usage. Are you using the LLM to understand human intent? Summarize outputs? Make a decision? Each use case carries a different risk profile and different levels of reliability of output/hallucinations. This will require varying levels of guardrails, testing, and human oversight.
Scrub before you send. Know exactly what data leaves your environment and reaches the model. Sensitive information such as PII, credentials, and proprietary sources should be stripped or masked before it ever hits an LLM API, not after.
The Agentic Layer
This is the layer founders often underestimate the most, and it’s the one I spend the most time on. Think of an AI agent as someone you’ve handed a power of attorney. Agency is the ability to act and make decisions on your behalf. The more agency you grant, the greater the risk. One bad decision by an agent can cascade into actions you never explicitly approved.
The fix is to put boundaries around them:
Define, per agent, the exact actions, functions, APIs, tools, and data it can touch. If an agent doesn't need write access to a system, it shouldn't have it.
Decide deliberately where a human needs to stay in the loop. This isn’t just a formality; it’s about judgment, responsibility, and accountability that a model cannot own.
Treat every expansion of an agent's permissions as a decision. Require documentation and explicit approval.
Most importantly, especially in a multi-tenant setup, ensure your agent acting on behalf of your user has access only to that user’s data and can only perform actions that the user can.
Security isn’t something you bolt on after launch. It must be designed from Day Zero, before the agent is ever given a task.
The Data Layer
Map how data flows through your system from ingestion, through the model and agent layers, to storage and output. Know who and what has access at each step. If you can’t draw this on a whiteboard in under five minutes, you don’t understand your own architecture well enough to defend it in an audit. This will also shape the blueprint of the infrastructure you need to deploy and the network diagram that your auditors will ask for.
The Guardrail Layer
A model cannot tell the difference between your instructions and instructions hiding inside a document or prompt it just read. That is prompt injection, and it is the number one risk in OWASP's Top 10 for LLM Applications. Sanitize what goes in, classify intent before you spend a token, and never pass model output into a downstream system unvalidated. Your WAF protects the web layer in front of the model, not the prompt itself, so assume you need both. Build the guardrails first, then red team against them. The only way to know a boundary holds is to attack it yourself.
Operational Controls
Ideally, a long-lived API key should never exist in the first place. Prefer short-lived secure credentials. Where a key is unavoidable, it belongs to a secret manager, scoped to one job, owned by a named person, and rotated on a schedule. Unattended, over-permissioned keys sitting in an organization for years are one of the first things an attacker looks for and one of the first things an auditor asks about. So, schedule the cleanups.
Log Everything
Every action an agent takes, every decision a model informs, needs to be logged for traceability. For agentic systems, this ensures explainability and aids in incident root cause analysis. Logs will exist at each layer: application, infrastructure, and cloud logs.
Authentication and Authorization
Don’t reinvent the wheel. Use established identity providers instead of building this yourself. Ensure users have MFA, and systems and applications use service accounts that follow least privilege.
Cost Controls
With AI, costs can escalate quickly. Set budgets and alerts so you can act immediately when usage spikes. There’s no need to go bankrupt before your product even takes off.
Don't Forget What's Outside the Product
Customers and auditors don’t just look at your product. They examine how your team operates daily. The basics still matter:
MFA for your logins
Full-disk encryption on every laptop
Regular, tested backups
Anti-malware tooling
VPN use for anything touching sensitive systems
These are unglamorous but necessary and easy to implement.
Non-Technical Controls Matter Just As Much
Auditors want to see that you’ve designed your controls well. This starts with solid policy and process documentation.
Policy
You need written infosec policies covering patching, vulnerability management, application code scanning, cloud configuration standards, backups, disaster recovery, incident response, and security awareness training.
Process
Two processes often overlooked by early-stage teams, yet asked about in nearly every audit, are:
Onboarding and Offboarding: Who gets access to what, and how fast is access revoked when someone leaves?
Background Checks: Do you run them, and is it documented?
Evidence Collection: The Part Nobody Explains Well
Auditors don’t just want to know you do these things and that you’ve designed the controls well. They also want to see that the controls are actually in use and operating effectively. This is where most founders get stuck because "evidence" sounds abstract until you see examples. Evidence is about proving that you’re following your own policies and processes. Here are a few concrete examples:
If your vulnerability management policy commits to fixing critical issues in one week, highs in three weeks, and mediums in two months, evidence means showing the tickets, timestamps, and closure dates that prove you actually hit those windows. These might be a report download from GitHub or your code scanning solutions.
For infrastructure, evidence means having an up-to-date architecture diagram alongside a clean configuration scan with no outstanding issues.
For disaster recovery, evidence can be as simple as a video recording of you actually testing your backup restore and disaster recovery process, for example, recovering the database from backup.
The Takeaway for Every Startup in My Network
Security and audit readiness is best started today. Bake security into the design of your agentic system. Integrate controls as policy and in your processes from Day Zero. It’s in your model choices, the boundaries you set for your agents, the policies you write down, and the processes you follow.
I was thrilled to see that such a tiny startup was thinking about security and compliance at this stage. Startups like them will get this right, not because they have more resources, but because they thought about it early enough and made it a habit instead of a fire drill.
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