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AI security guide

Securing AI &
LLM Applications

Generative AI created a brand-new attack surface that traditional controls don't see. Here's what's different, the risks that matter, and the defenses that actually hold.

Updated June 2026 ~8 min read OWASP-aligned
What's inside
  1. Why AI is a new surface
  2. The risks that matter
  3. Shadow AI
  4. Defenses that work
  5. What an LLM firewall does
  6. AI security checklist

1. Why AI is a genuinely new surface

Classic appsec assumes code and data are separate. Large language models break that assumption: instructions and data arrive in the same channel — natural-language text. An attacker can smuggle commands inside content the model reads (a web page, a document, an email), and the model may follow them. Add non-deterministic outputs, third-party model APIs, and employees pasting sensitive data into chatbots, and you have risks that firewalls and WAFs were never designed to catch.

2. The risks that matter (OWASP LLM Top 10)

The OWASP Top 10 for LLM Applications is the best shared vocabulary. The ones that bite most teams:

Prompt injection

Malicious instructions hidden in user input or in content the model retrieves (indirect injection) override your system prompt — the #1 LLM risk.

Sensitive information disclosure

The model reveals secrets, PII or proprietary data — from its context, training data, or connected tools.

Insecure output handling

App code trusts model output and passes it into a shell, SQL query, or browser — turning a hallucination into RCE or XSS.

Excessive agency

An AI agent has more tools/permissions than it needs, so a successful injection can take real, damaging actions.

Supply chain & model risks

Poisoned training data, tampered models from public hubs, or vulnerable AI libraries.

Unbounded consumption

Cost and denial-of-wallet attacks that hammer expensive model calls.

3. The one most orgs miss: shadow AI

Before you secure the AI apps you built, find the ones your people are using. Employees paste source code, customer data and strategy docs into consumer chatbots and browser extensions every day. This shadow AI is often the biggest real-world leak — and it's invisible unless you're looking for it at the endpoint and network.

  • Discover unsanctioned AI tools in use across the org.
  • Govern with a clear acceptable-use policy and sanctioned alternatives.
  • Inspect what data is actually flowing to AI endpoints.

4. Defenses that actually work

Treat all model input and output as untrusted

  • Inspect prompts for injection patterns and sensitive-data leakage before they reach the model.
  • Never pass raw model output into a shell, SQL, eval, or the DOM — validate and encode like any untrusted input.
  • Keep system prompts and secrets out of reach; assume users will try to extract them.

Constrain what the AI can do

  • Least privilege for AI agents — minimum tools, scoped credentials, human approval for high-impact actions.
  • Rate-limit and budget model calls to blunt denial-of-wallet.
  • Vet models and AI libraries like any supply-chain dependency.

Govern the data

  • Classify what may and may not go to AI; enforce it with DLP-style inspection.
  • Self-host or bring-your-own-model where data sensitivity demands it, so prompts never leave your control.

5. What an "LLM firewall" actually does

An LLM firewall sits in the path of AI traffic and inspects it in real time — much like a WAF for the model layer. A good one will:

  • Detect and block prompt-injection attempts in inbound prompts.
  • Catch sensitive data (secrets, PII) heading to a model and block or redact it.
  • Flag risky or non-compliant responses coming back.
  • Give you an audit trail of AI usage for governance.
How Kaimz helps. The platform's AI-era defenses include a runtime LLM firewall (prompt-injection + data-leak inspection for AI traffic) and shadow-AI discovery that surfaces unsanctioned AI usage straight from the endpoint — plus a non-human-identity inventory for the keys and tokens AI agents run on. See the platform →

6. AI security checklist

  • Inventory AI apps you build and shadow AI your people use.
  • Inspect prompts for injection + sensitive data before the model.
  • Validate/encode model output before code ever trusts it.
  • Least-privilege tools + human-in-the-loop for AI agents.
  • Acceptable-use policy + sanctioned tools; data-classification enforced.
  • Vet models/libraries; rate-limit and budget model calls.
  • Log AI usage for audit; self-host where data sensitivity requires.
Next step

Bring AI traffic under control

See AI-era defenses More guides