AI Security Scanner Platform

The cybersecurity landscape in 2023-2024 revealed a critical gap in available security tooling. Traditional web application scanners like Nessus, Qualys, and Burp Suite excelled at detecting conventional vulnerabilities: SQL injection, XSS, CSRF, misconfigured headers, and outdated SSL/TLS. But they were completely blind to the new attack surface introduced by AI systems. Conversely, the emerging AI security tools focused narrowly on LLM-specific risks like prompt injection and training data extraction, but ignored the traditional web security fundamentals that still account for the majority of real-world breaches.

Enterprises deploying AI systems faced an impossible choice: run two separate scanning tools with no correlation between findings, or accept blind spots in their security posture. Most chose the latter, unknowingly leaving their AI-powered applications exposed to attacks that exploited the intersection of traditional and AI-specific vulnerabilities. For example, an API endpoint serving an LLM inference service might be vulnerable to both conventional authentication bypass and prompt injection, but no single tool would flag both issues or help security teams understand the compounded risk.

BeyondScale needed a platform that could scan a single domain and deliver a unified security assessment covering DNS security, HTTP headers, SSL/TLS configuration, WAF detection, hosting analysis, AI endpoint discovery, prompt injection testing, LLM vulnerability assessment, AI compliance scoring, and AI data exposure analysis, all in under 60 seconds.

We built Securetom as a modular scanning platform with 10 independent modules that share a common orchestration layer and unified reporting engine. The architecture prioritizes speed through parallel execution, accuracy through module-specific validation, and actionability through integrated compliance mapping.

The traditional security modules cover five domains. The DNS Security Analyzer examines A, AAAA, MX, NS, TXT, and SOA records, checking for zone transfer exposure, SPF/DKIM/DMARC configuration, subdomain enumeration risks, and dangling DNS entries that could enable subdomain takeover. The HTTP Security Headers module validates 15 header categories including Content-Security-Policy, Strict-Transport-Security, X-Frame-Options, X-Content-Type-Options, Referrer-Policy, and Permissions-Policy, scoring each against OWASP best practices. The SSL/TLS Analyzer tests certificate validity, chain completeness, cipher suite strength, protocol version support (flagging TLS 1.0/1.1), and checks against known vulnerabilities like BEAST, POODLE, and Heartbleed. The WAF Detection module uses a fingerprinting library of 152+ signatures to identify web application firewalls including Cloudflare, AWS WAF, Akamai, Imperva, F5, and dozens more, helping security teams understand their perimeter defenses. The Hosting and Infrastructure module identifies hosting providers, CDN configurations, server technologies, and potential information disclosure through server headers and error pages.

The AI security modules extend coverage into five additional domains. The AI Endpoint Detection module crawls the target domain looking for exposed LLM and ML API endpoints by analyzing URL patterns, response headers, and payload structures characteristic of inference APIs. The Prompt Injection Scanner tests discovered AI endpoints with a curated library of 200+ injection vectors covering direct injection, indirect injection via document uploads, jailbreak attempts, instruction override, and role manipulation. The LLM Vulnerability Assessment evaluates AI endpoints for system prompt extraction, training data memorization, PII leakage through completions, and excessive agency in tool-calling configurations. The AI Compliance Scorer maps findings against EU AI Act requirements, NIST AI RMF controls, OWASP LLM Top 10, and ISO 42001 standards, producing a compliance readiness score with specific gap identification. The AI Data Exposure Analyzer examines whether AI systems inadvertently expose sensitive data through model outputs, embedding similarities, or retrieval-augmented generation (RAG) pipeline leaks.

The orchestration layer manages parallel execution of all 10 modules, handling rate limiting, retry logic, and timeout management. Results are aggregated into a unified scoring model that weights findings by severity, exploitability, and business impact. The reporting engine generates actionable dashboards with drill-down capability from executive summary to individual finding detail, including remediation guidance and compliance mapping for each issue.

Securetom is deployed as a SaaS platform with three tiers: a free single-scan tier for domain assessment, a Pro tier with continuous monitoring and scheduled scans, and an Enterprise tier with custom scanning policies, API access, and dedicated support.