What Is URL Inspection?
URL inspection parses, normalizes, and analyzes destinations — status codes, redirects, and metadata — for link health and agent destination controls.
Blog / AI Security
AI Security
Cross-cutting AI application security — RAG pipelines, vector stores, runtime controls, and defense in depth.
How to Detect Suspicious URLs Programmatically
Detect suspicious URLs programmatically with parsing, normalization, redirect analysis, and metadata inspection — using real URL Inspector capabilities.
Phishing Detection API: What Developers Need
Evaluate phishing detection APIs — URL analysis, content signals, false positives, latency, and integration without overstating URL-only capabilities.
Scam Detection for AI-Powered Applications
Scam detection for AI apps — content signals, phishing overlap, moderation boundaries, and limits of classifier-based evaluation.
How to Protect API Keys in SaaS Applications
Protect API keys in SaaS — server-side secrets, environment variables, rotation, log redaction, browser exposure risks, and CI secret handling.
API Key Rotation Best Practices
API key rotation best practices — overlapping keys, revocation, automation, compromised-key response, without inventing compliance intervals.
API Security Checklist for SaaS Developers
A production API security checklist for SaaS — authentication, authorization, rate limits, validation, secrets, logging, tenant isolation, and monitoring.
OWASP Top 10 for LLM Applications: Developer Guide
OWASP Top 10 for LLM applications — developer-oriented summary of current GenAI risks, mitigations, and how they map to production controls.
How to Threat Model an LLM Application
Threat model an LLM application — assets, entry points, trust boundaries, data flows, controls, and testing with a practical template.
AI Red Teaming for SaaS Developers
AI red teaming for SaaS developers — defensive test planning, adversarial cases, injection, data leakage, tool misuse, and regression testing.
How to Build an AI Security Test Suite
Build an AI security test suite — fixture categories, expected verdicts, edge cases, false positives/negatives, and regression tracking.
Security Testing Before Launching an LLM Feature
Pre-launch security testing checklist for LLM features — injection, PII, output safety, rendering, tools, rate limits, and failure modes.
False Positives vs False Negatives in AI Security
False positives vs false negatives in AI security — definitions, product impact, threshold trade-offs, review states, and evaluation datasets.
Deterministic Rules vs LLM-Based Security Classifiers
Deterministic rules vs LLM-based security classifiers — when each fits, hybrid architectures, and why neither alone is sufficient.
Designing Allow, Review and Block Security Decisions
Design allow, review, and block security decisions — why ternary verdicts beat binary flags for high-impact operations and human review.
AI Security Checklist for Production SaaS
A production AI security checklist for SaaS — authentication, injection, PII, output, agents, rate limits, logging, monitoring, and incident response.
The Production AI Security Stack: Inputs, Outputs, Data and Agents
The production AI security stack — input security, data protection, output safety, agent controls, guardrails, logging, testing, and provenance.
RAG Security: Risks Every Developer Should Know
RAG security risks — untrusted documents, indirect prompt injection, data poisoning, access control, sensitive retrieval, and unsafe output handling.
Securing Retrieved Documents Before They Reach an LLM
Secure RAG retrieved documents with trust boundaries, source validation, content scanning, authorization, and instruction/content separation.
Vector Database Security for LLM Applications
Vector database security for LLM apps — tenant isolation, authorization, sensitive embeddings, poisoned sources, and retention at the application layer.
RAG Data Poisoning Explained
RAG data poisoning — how compromised knowledge sources influence retrieval, validation strategies, trusted ingestion, provenance, and monitoring.