Build a guarded AI pipeline
Create a policy boundary for data, models, outputs, and budgets without handing your application’s authority to a classifier.
6 min readRead the guide
SECURITY, WITHOUT THE GUESSWORKBig ideas.
Big ideas.
Stronger
shields.
Clear thinking for API security and AI protection. Explore the boundaries that keep requests in check, data in scope, and powerful models on task.
Practical guides. Clear examples. No black boxes.
Every request has a boundary.DEFENSE IN DEPTH
Powerful AI. Deliberate limits.BUILT AROUND BETTER DECISIONS
Identity Contracts Context Outputs
FIVE TOPICS. ONE CLEARER PICTURE.
Find your starting point A shield for every boundary.
From the first authenticated request to the final AI action, understand what belongs where—and why it matters.
01 / 05
API shield
Connect identity, contracts, permissions, and resource controls into a defense you can explain.
Explore the boundary02 / 05
API security
Keep authentication, authorization, and release evidence connected—not confused.
Explore the boundary03 / 05
AI shield API
Put data, model access, output handling, and task budgets inside a deliberate policy boundary.
Explore the boundary04 / 05
LLM API shield
Treat prompts and generated tool calls as untrusted content—not permission to act.
Explore the boundary05 / 05
API firewall shield
Validate expected requests early, then keep business permissions where the right context lives.
Explore the boundaryEXPLORE THE DECISIONSee the boundary.
See the boundary.
Understand the why.
A well-formed request is only the beginning. Identity, permissions, and available resources each have a say in what happens next.
- Validate the shape.
Does the request match the supported contract?
- Keep authority explicit.
May this caller act on this particular resource?
- Budget the whole task.
Can the work run within a deliberate allowance?
ILLUSTRATIVE REQUEST
POST /v1/reports/summarize
{
"document_id": "report_demo",
"max_output_tokens": 600
}ALLOW · Expected request
Example: authorized document, valid shape, available task budget.
Fixed examples. Nothing is submitted.
API SECURITY MEETS AILet AI do more.
Let AI do more.
Not whatever.
A useful AI boundary goes beyond the prompt. Keep context authorized, tools scoped, outputs checked, and the whole task inside a budget.
Explore the AI shield01
Before the model
Verify the caller. Retrieve permitted context. Remove what the task does not need.
02
Around the model
Choose approved capabilities. Bound tokens, retries, concurrency, and tool steps.
03
Before the action
Validate the proposal. Recheck authority. Require exact approval where needed.
THE SHIELD JOURNAL
Read all 10 guides Less noise. Better defenses.
Practical, long-form thinking for the people building the next request, integration, or AI workflow.
Prompt injection: defend the boundaries
Keep untrusted content separate from authority with scoped retrieval, constrained tools, output checks, and end-to-end tests.
6 min readRead the guide
Better logs. Clearer incident response.
Design minimized decision events, actionable alerts, and a containment exercise that distinguishes attempts from completed actions.
6 min readRead the guide
GOOD QUESTIONS. CLEAR ANSWERS.Get your
Get your
bearings.
Start with the important distinctions. Then go deeper in the guides.
Open the glossaryWhat does an API shield actually protect?
Think of an API shield as coordinated request boundaries: identity, permission, shape, resource use, and output handling. Each boundary needs its own enforcement and tests. The architecture guide explains where those decisions belong.
How is API security different from a firewall?
A firewall can inspect or constrain supported traffic. API security also includes application-specific permissions, credential lifecycles, business state, data exposure, and response operations. A valid request can still be unauthorized.
What is an AI shield API?
An AI shield API is a policy boundary around an AI workflow. It can coordinate approved data access, model selection, output contracts, and task budgets. Important permissions and execution decisions should remain in independently enforced application rules.
Can an LLM API shield stop every prompt injection?
No single prompt filter should be treated as a complete guarantee. A more useful design also constrains retrieval, tool authority, outbound destinations, and resource consumption, then tests end-to-end consequences.
Where should I start?
Choose one sensitive workflow. Identify its callers, resources, allowed actions, and failure behavior. The quickstart playbook helps turn that map into a small collection of concrete tests and ownership decisions.
Does the policy explorer process my API traffic?
No. It switches between fixed examples entirely in your browser. It does not connect to a backend, inspect real traffic, or enable protection for another application.
Make the next request a better one.
Start with a clear architecture. Build toward boundaries you can test.