This is exactly how you start, and it's a great first step. OPA/Rego for agent action validation is a fantastic fit.

A gap I'd watch for is parameter validation beyond just the tag pattern. For example, your `container_update` policy might allow the action and check the tag, but does it also validate the full image name? You don't want a request slipping through for `nginx:stable-alpine` if you only meant to allow updates for your internal `myapp/backend` images. Adding an `allowed_image_prefixes` list might be a logical next move.

Also, think about adding a simple `deny` default rule at the top of your policy package. It's a safety net for any action type you haven't explicitly allowed. Nice work sharing it, I'll take a look at the Git repo

Your approach is the right one. Starting with the explicit allowlist for actions and targets is the foundation. A lot of people skip that and jump straight to complex logic, which is backwards.

> I got nervous about it having too much power

This is the correct instinct. Your policy should act as your nervous system, saying "no" by default. user100's suggestion for a top-level `deny` rule is key. Your `allowed_image_prefixes` list should be empty until you fill it.

One thing I'd add: think about *who* is making the request, not just *what*. Is the LLM calling your policy endpoint directly, or is there a service in between? Log the full input the policy receives, not just the final decision. It'll help you debug when a valid-looking request gets blocked and you need to see why.

Starting with the explicit allowlist is exactly right. That initial constraint gives you a clear foundation to build on.

I'd suggest adding a simple time-based constraint early, even if it's just a weekend blocker. For example, deny any `container_update` action between 2 AM and 5 AM local time, which you can define with a helper rule checking `time.clock()`. It's a trivial addition in Rego, but it forces you to think about the temporal context of actions, which is often overlooked.

Also, be mindful of the order of your rules. If you later add more complex deny logic, make sure your default deny is evaluated last.

Welcome to the forum, Hal, and thanks for sharing your work. Starting with those explicit allowlists is absolutely the right call - that's your primary safety mechanism right there.

> it made me feel a lot safer
That's the best feedback your own system can give you. Trust that feeling. The gap I often see in first policies is forgetting to validate the *input* itself, not just the rules. Make sure your policy also checks that the request contains all the required fields (action, target) in the correct format. A malformed request that passes a null to your policy might skip your logic entirely.

Looking forward to seeing the repo.

Absolutely, user61's point about validating the input structure itself is huge. I learned that the hard way when I first plugged OPA into a little Flask wrapper. My policy logic was solid, but if the incoming JSON had a typo like `"acton": "restart"` instead of `"action"`, my entire allowlist check was evaluating against `null`. The request would sail through because none of my deny rules had anything to latch onto.

It feels so obvious in hindsight, but you get focused on the fancy logic and miss the basics. My fix was a boring `input_valid` rule at the very top that just checks `input.action` and `input.target` are present and strings, defaulting to deny if not. It's the policy equivalent of checking your helmet strap before you get on the bike.

That's the exact failure mode of most Rego policies I review. The evaluation of a missing field against an allowlist often returns `null`, which isn't `false`, so the deny rule never fires.

A more defensive pattern is to define the allowlist check as a function that returns false on any invalid input, then use that function in your logic. For example:

```
allowed_action(action) {
action := input.action
action == "service_restart"
} else = false {
true
}
```

Now if `input.action` is missing, `allowed_action` is false, not undefined.

The initial allowlist approach you've described is fundamentally sound. It establishes the necessary least-privilege boundary. The subsequent suggestions about input validation and default deny rules are critical operational additions.

Where I'd encourage you to think next is the provenance of the request itself. Your policy evaluates `input`, but what guarantees the integrity of that input? In a lab setup, the LLM might call a local API which then consults OPA. You should consider adding a simple check, even if just a hard-coded token in the initial phase, to assert the request is coming from your designated broker service and not from another process or a direct curl command. This moves you from validating the *request* to validating the *request channel*, which is a logical next layer.

Your tag pattern check (`stable-*`) is a good start, but it's a string operation on untrusted data. Be mindful that pattern matching in Rego, depending on how you've implemented it, could be susceptible to bypasses if the attacker controls the input string. Using `regex.match` with anchored patterns (e.g., `^stable-[a-z0-9]+$`) is more robust than a simple `contains` or glob-style check.

Good call on the request channel. I slapped a JWT check in my API wrapper that calls OPA. It's just a shared secret for now, but it means the *only* thing that can even ask for a policy decision is my broker service. The LLM talks to the broker, broker talks to OPA.

Your point about the regex is spot on. My first pass used `contains` on the tag, which is useless. Switched to `regex.match("^stable-[a-z0-9]+$")` and it actually means something. Appreciate the tip.

That JWT check is a solid step, but be careful about where you store and validate that shared secret. If it's just an environment variable in your broker, you've moved the trust boundary, not eliminated it.

The regex improvement is critical. `contains` is a policy smell; it almost always indicates a flawed security model. A proper regex anchor is the minimum. The next level is to validate the tag against a known, signed list from your registry or build system, so you're not just checking format but provenance.

Ugh, that missing-field-evaluates-to-null trap is a classic. I set up a monitoring rule just for that in my lab policy after something similar bit me.

It's easy to think your fancy logic is airtight, then a typo lets everything through because `null` isn't in your deny set. My "boring" top-level check is a single line now: `deny["invalid input format"] { not input.valid_format }`. The `valid_format` rule just ensures the required keys exist and are the right type. Boring is beautiful.

And yeah, it's exactly like checking the helmet strap. You don't think about it until you need it, and then you really, really need it.

Oh man, that helmet strap analogy is perfect. I had the exact same "oh no" moment when I was testing my first policies with a little Python script. I accidentally sent a JSON payload with `"target" : null` and watched it get a green light. My entire deny rule for `target not in allowed_targets` just... evaporated.

That's when I adopted the boring `valid_input` rule as step zero, like you said. But I also added a debug deny line that fires *specifically* on missing or null fields, just so my decision logs scream at me if it happens again. Something like:

```
deny["input.action is null or missing"] {
not input.action
}
```

It's redundant with the format check, but seeing that exact message in the logs saved me an hour of head-scratching last week when my agent's API client had a bug. The boring stuff needs loud alarms too.

That redundant debug deny rule is such a good idea. I copied the same "boring" valid_input check from earlier in the thread, but I didn't think to add the explicit logging for each field.

It turns a silent "allow" from a null into a loud error in the decision log. I'm going to add those lines tonight for action, target, and source. It's exactly the kind of early warning I want when I'm testing my local agent.