AI threats are in runtime. Your defenses should be in real-time.

AI guardrails are real-time security controls that monitor and enforce safety policies on AI applications during production runtime. Unlike pre-deployment testing, guardrails inspect every user prompt, model reasoning step, and tool call as they happen by blocking threats like prompt injection, data leaks, hallucinations, and agent manipulation before they reach users or downstream systems.

Straiker Defend AI implements context-aware guardrails that adapt continuously without code changes or performance trade-offs. The platform uses a compact, optimized inference engine to deliver subsecond threat detection and blocking across multimodal inputs including text, code, images, PDFs, and Microsoft Office documents. Built-in guardrails come privacy-preserving and can be customized to match your organization's specific policies and use cases.

No. Straiker Defend AI's runtime guardrails are designed for production speed with subsecond detection and blocking that scales automatically. The detection engine uses a compact, optimized inference engine specifically built to make security decisions without introducing latency that users would notice. Implementation requires just one line of code via API, SDK, log forwarder, or AI sensor, which means no refactoring or infrastructure changes needed. The system runs parallel to your AI application's normal operations, inspecting prompts and responses in real-time while maintaining the fast, responsive experience your users expect from AI agents and chatbots.

Multimodal AI security protects against threats that span different input types—text, code, images, PDFs, Microsoft Office documents, and mixed media. Traditional security tools often create blind spots by only monitoring text prompts, but modern AI agents process diverse content types where attackers can hide malicious instructions in images, embedded documents, or mixed-format inputs.

Defend AI provides consistent protection across all input modalities with unified policy coverage, shrinking blind spots and surfacing hidden attack patterns. This is critical for enterprise AI applications where users upload documents, share images, or interact through complex workflows that combine multiple content types. The platform's multimodal support ensures no attack vector goes unmonitored, regardless of how threats are delivered.

Runtime AI guardrails prevent threats that occur during live production interactions, including prompt injection attacks, mass data leakage and exfiltration (PII, PCI, HIPAA), agent manipulation and intent drift, hallucinations and harmful content generation, toxic output and policy violations, and rogue agent behavior caused by excessive autonomy. The system also provides multimodal threat detection across text, code, images, and file uploads.

Runtime guardrails prevent hallucinations by using real-time LLM safety controls that analyze model outputs before they reach users or downstream systems. Defend AI can detect when an AI agent is generating fabricated information, contradictory statements, or responses that drift from factual grounding, then either rewrite the output to be accurate, suppress it entirely, or flag it for human review.