AIBIM AI: First Look at the Runtime Security Layer for AI Interactions

What AIBIM AI actually does

AIBIM AI positions itself as a policy enforcement layer that sits between your AI applications and the model providers you call (OpenAI, Anthropic, etc.). Instead of trusting that every prompt, tool call, and model response is safe by default, AIBIM inspects that traffic in real time and can block, redact, or log it according to rules your team defines.

Concretely, that means three things happen at runtime:

1. Prompt inspection — outbound prompts are scanned before they reach the model, so you can catch someone pasting a customer's SSN into a chat window or an internal doc into a public model.
2. Tool-call inspection — for agentic workflows where a model can trigger actions (send an email, hit an API, write to a database), AIBIM can intercept the call and check it against policy before it executes.
3. Response inspection — model outputs are checked on the way back, which matters if a model is coaxed into leaking a system prompt or generating content that violates a compliance policy.

Everything that passes through gets logged, which is the part aimed at compliance and security teams who need an audit trail of "what did our AI tools actually do" rather than just "what did we configure them to do."

Deployment options

AIBIM ships in three forms, which is unusual for a category that's mostly SDK-only right now:

• Desktop application — for monitoring AI usage across a workstation, useful for locking down what employees paste into web-based AI tools like ChatGPT or Claude's web UI.
• Browser extension — enforces policy specifically on browser-based AI interactions (chat UIs, web copilots) without requiring IT to touch the underlying app.
• Application SDK — for teams building their own AI features who want to embed the same inspection/blocking logic directly into their product's request pipeline.

That spread suggests AIBIM is trying to cover both the "shadow AI" problem (employees using consumer AI tools with company data) and the "we're building AI features and need a security layer" problem, with one policy engine underneath both.

Concrete use cases

• A support team's agents use ChatGPT to draft replies. AIBIM's browser extension blocks or redacts customer PII (card numbers, emails, order IDs) before it's pasted into the prompt box.
• An engineering team is building an internal coding assistant on top of an LLM API. They use the SDK to intercept tool calls so the assistant can't execute a database write or a shell command without passing a policy check first.
• A compliance team needs to show an auditor exactly what data left the company via AI tools over the last quarter. AIBIM's logging layer produces that trail without requiring each team to instrument their own tools.
• A security team wants a kill switch for a specific risky prompt pattern (e.g., attempts to extract system prompts or jailbreak an internal assistant) without waiting on a model provider's own guardrails to catch it.

Pricing: not published

AIBIM AI does not list pricing on its site as of this writing. That's typical for early-stage runtime security tooling aimed at teams, where pricing tends to depend on seat count, request volume, or deployment scope (desktop-only vs. SDK-wide). If you're evaluating it today, expect to book a demo or sales call rather than self-serve signup, and come prepared with rough numbers on how many users or how much request volume you'd be putting through it — that's almost certainly how they'll scope a quote.

If budget certainty matters more than fit right now, that's worth weighing against alternatives below that publish at least a starting tier.

How it compares

Product	Approach	Deployment	Price	Best for
AIBIM AI	Runtime policy enforcement: inspects prompts, tool calls, and responses live	Desktop app, browser extension, application SDK	Not published — sales/demo only	Teams that need one control layer across both employee AI usage and in-house AI products
Lakera AI (Lakera Guard)	Runtime detection API focused on prompt injection, jailbreaks, and data leakage	SDK/API integration into your own app	Free developer tier; paid and enterprise tiers, custom pricing	Dev teams hardening a specific LLM-powered app against prompt injection
Robust Intelligence (now part of Cisco)	AI model risk assessment and validation, spanning pre-deployment testing plus runtime protection	API/platform integration, typically enterprise-deployed	Enterprise custom pricing	Larger orgs needing formal AI risk assessment across the ML/AI lifecycle, not just chat traffic
Anthropic's Constitutional AI	Training-time alignment technique built into Claude's model weights	Not deployable separately — it's baked into the model, not a tool you install	N/A — not a purchasable product	Teams that just want a model with built-in alignment behavior, not an external enforcement layer

A note on that last row: Constitutional AI isn't really a competitor in the product sense — it's a training methodology Anthropic uses to shape Claude's behavior before it ever reaches you. It shows up in comparisons because people often ask "why not just rely on the model provider's own safety training instead of adding a layer on top," and the honest answer is that it solves a different problem. It can't inspect your prompts for a customer's SSN, it can't stop a tool call to a production database, and it doesn't give you a company-side audit log. If you need that, you need a runtime layer like AIBIM, Lakera, or Robust Intelligence — Constitutional AI operates upstream of all of them.

Should you try it today

AIBIM AI is worth a look if your actual problem is uncontrolled AI usage across a team — people pasting sensitive data into consumer AI tools, or an internal agent making tool calls you can't audit after the fact — and you want one system that covers both the browser-based shadow-AI problem and the SDK-level "we built this ourselves" problem. The three-surface deployment (desktop, browser, SDK) is genuinely broader than most competitors in this space, who tend to pick one integration point.

The catch is the missing pricing page. If you're comparison-shopping on cost, Lakera at least gives you a free tier to start testing prompt-injection detection today, while AIBIM and Robust Intelligence will require a sales conversation before you know what you're paying. Budget the time for that conversation before you commit to an eval, and ask specifically how they price the desktop/browser/SDK components — separately or as one bundle — since that will materially change the cost depending on which surfaces you actually need.