Generative AI introduces an entirely new attack surface — your prompts, your data and your models. NITPRA wraps every AI interaction in a real-time security and governance layer, so you can deploy with confidence.
Traditional security tools weren't built for natural-language attacks. These are the risks we neutralize.
Malicious instructions hidden in user input or retrieved documents that hijack model behavior.
Sensitive data, PII or secrets exposed through prompts, responses or training data.
Crafted prompts that bypass safety controls to produce harmful or off-policy output.
Confidently wrong answers, unsafe tool calls and excessive agency in autonomous agents.
Mapped to the OWASP Top 10 for LLM Applications, MITRE ATLAS and the NIST AI RMF.
Six coordinated controls that inspect, protect and govern every model interaction — in real time.
Inline inspection of every prompt and response. Block injection, jailbreaks, toxic content and off-policy output before it reaches a user or your model.
Detect and redact PII, secrets and regulated data in both directions. Enforce data-residency and tenant-isolation rules automatically.
Every model call passes through one governed gateway with authentication, rate-limiting, key management and per-request policy enforcement.
Continuous, automated and human-led attacks against your AI to surface weaknesses before adversaries do.
Real-time traces, anomaly detection and alerting on every interaction. SIEM integration so AI events live alongside the rest of your security telemetry.
Least-privilege access for AI, scoped tool permissions, human-in-the-loop approvals and full audit logging for autonomous agents.
We place a security layer between your applications and your models. Nothing reaches a model — or comes back to a user — without passing policy. It deploys as a gateway, a sidecar or an SDK, in your cloud or ours.
We design controls against the industry-standard catalog of LLM application risks — here's how the major categories are covered.
| Risk | What it means | NITPRA control |
|---|---|---|
| Prompt injection | Untrusted input overrides instructions | Input guardrails · context isolation |
| Sensitive info disclosure | Model leaks PII, secrets or IP | DLP · redaction · output filtering |
| Supply-chain risk | Compromised models or components | Model vetting · SBOM · provenance |
| Improper output handling | Unsafe use of model output downstream | Output validation · sandboxing |
| Excessive agency | Agents do more than intended | Scoped permissions · approvals |
| System-prompt leakage | Internal instructions exposed | Prompt hardening · response checks |
| Unbounded consumption | Cost & denial-of-wallet attacks | Rate limits · quotas · budgets |
Controls are only half the story. We give you the governance, evidence and reporting to satisfy auditors, regulators and your own risk committee.
Start with a focused AI security assessment. We'll red-team your current AI, map findings to OWASP and NIST, and hand you a prioritized remediation plan.