AI red team SaaS

AI Red Team SaaS for Continuous Testing

AI red team SaaS gives teams a way to run adversarial testing on every meaningful change: prompts, models, tools, retrieval content, and policy logic.

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Where it fits

  • A startup needs credible AI security evidence before an enterprise sales review.
  • A security team wants broad testing without building its own attack harness.
  • A platform team wants a private deployment option for sensitive applications.

Operational steps

  • Start with baseline attack packs that reflect real prompt injection and jailbreak behavior.
  • Add custom cases from incidents, bug bounty reports, internal policies, and customer requirements.
  • Run scheduled scans and PR checks, then compare severity drift over time.
  • Export executive and engineering reports from the same evidence set.

Common risks

  • One-off red team results become stale after the next model or prompt update.
  • Custom attacks live in spreadsheets and never become regression tests.
  • The team cannot prove which issues were fixed before a release shipped.

How PromptGuard Scan fits the workflow

PromptGuard Scan brings red team cases into a SaaS workflow with templates, CI/CD gates, PDF reports, API access, and private deployment support for enterprise buyers.

Ready to test a real AI surface?

Pricing

Team annual is selected by default.

Annual billing is 50% off. All plans use NOWPayments checkout and keep the product page open.

Dev

For solo builders validating one product before launch.

$25/mo
$294 billed yearly. Save 50%.
5 apps500 scans
  • Prompt injection scans
  • Jailbreak template checks
  • PII and key leak detection
  • HTML risk report
  • Email support

Enterprise

For platform teams, private deployments, and audit-heavy AI systems.

$250/mo
$2,994 billed yearly. Save 50%.
Unlimited appsUnlimited scans
  • Everything in Team
  • Private deployment path
  • Custom test packs
  • Compliance evidence exports
  • Priority security review support

Security playbooks

Practical guides for LLM app security decisions.