One platform.
Multiple layers of AI security.
Discover weaknesses across your models, detect unsafe outputs in production, and turn every finding into evidence your team can act on - from a single API.
Discover
Detect
Report
Protect
Monitor
Govern
Security shouldn't live in a PDF.
Give your developers the tools to build security directly into AI applications, without rebuilding the stack.
Response monitoring
Analyze AI outputs as they're generated — not just at deploy time. Every response is scored before it reaches a user.
Threat detection
Identify suspicious or unsafe response patterns across prompt injection, leakage, hallucination, and adversarial behavior.
Output security controls
Introduce additional controls around what your AI application is allowed to return, with configurable enforcement.
Model risk evaluation
Test models against defined security and safety scenarios before they ship, and after every material change.
Security logging
Every scan produces a structured, queryable security event — built for investigation, not just alerting.
Policy enforcement
Apply organization-specific AI security policies consistently across models, teams, and environments.
Three lines between you and a scored response.
Wrap any model call - LLM, RAG pipeline, or autonomous agent - and get a verdict, a severity score, and the evidence behind it, synchronously.
1import { AslanGuard } from "@aslanguard/sdk";23const guard = new AslanGuard({ policy: "default" });45const result = await guard.scan({6 input: userPrompt,7 output: modelResponse,8 context: { app: "support-agent" },9});1011if (result.verdict === "blocked") {12 return guard.fallbackResponse(result);13}
Guardrails are not enough.
AI security can't stop at defining what a model is allowed to do. You need to understand what it actually does - across prompts, outputs, tools, agents, and real-world interactions.
One weak signal is noise. Four converging signals are proof.
Every prompt, output, and interaction leaves its own trail of evidence. We correlate threat intelligence across injection attempts, leakage patterns, and anomalous behavior in real time — fusing weak signals into one defensible security decision before anything reaches a user.
Models and agents don't secure themselves. This layer does.
Alignment evaluation, agentic red teaming, adversarial testing, and policy enforcement don't have to live in separate tools and separate teams. Wrapped around every model and agent, they run continuously as one coordinated layer of oversight.