AI Security
The security layer for AI-era apps — where the clients are agents and the attack surface is your API. SecKav governs which AI agents reach which tools, blocks prompt-injection and object-level authorization abuse, and controls how AI crawlers use your content.
MCP firewall
If you expose tools to LLMs over the Model Context Protocol, SecKav sits in front and enforces which registered agent may call which tool, with intent-aware policies. Every tool call is identity-checked and logged.
textagent "support-copilot" → allow: read_ticket, search_kb deny: refund, delete_user (requires human approval)
AI-crawler control
Decide how AI crawlers (GPTBot, ClaudeBot, PerplexityBot, …) use your site: allow, block, or serve a restricted view. Bot Management scores and enforces this at the edge.
Self-learning object authorization (BOLA)
Broken Object-Level Authorization is the #1 API risk. SecKav learns which principal owns which object from your traffic (or from explicit ownership claims via the server SDK) and blocks cross-tenant object access — even when your app forgets the check.
tsimport { SecKavServer } from '@seckav/sdk/server'; const seckav = new SecKavServer('sk_live_…'); // Claim ownership when an object is created — SecKav enforces it thereafter. await seckav.claimResource('invoice:456', req.user.id);
Prompt-injection & payload defense
Inbound payloads to AI endpoints are normalized (the same evasion-folding the WAF uses) and screened for injection patterns, so a hidden instruction in user content can't hijack your model calls.
Autonomous zero-day patching
SecKav polls CVE feeds, drafts a virtual patch, deploys it to the edge in log-only mode, and promotes it to blocking only after a soak with a low false-positive rate — so new classes of attack are covered before you've read the advisory.