Research · Safety & governance
Traditional security isn't enough for AI. FlowX.AI adds AI-native defenses — PII redacted before the model, outputs re-scanned, everything grounded and governed.
pii redacted before the model · fail-closed
Trustworthy enterprise AI is a property of the harness around the model — not of the model itself.
You don't make an LLM safe by hoping it behaves. You wrap it in preventive, detective and corrective controls — redact what it shouldn't see, ground what it says, scan what it returns, and keep a person in the loop where it matters. Safety is engineered around the model.
OWASP Top 10 for LLMs · Observatory guardrailsPersonal data is guarded at every boundary of an AI node — before the model, at the model, and after it. Enabled per node, off by default, and fail-closed.
Personal data in chat, documents and images is detected and replaced with typed <PII/> placeholders before it ever reaches the model — a hybrid of regex for structured PII and ML for names and addresses.
The model only ever sees redacted input; the original values stay inside the platform. LLM calls are stateless and your data is never used to train foundation models.
Responses are re-scanned on the way out — catching PII the model may have generated or even hallucinated — before anything flows downstream. If a scan fails, the node halts.
Observatory guards every AI interaction with input and output scanners, aligned to the OWASP Top 10 for LLMs.
Input scanners detect and neutralize malicious instructions, toxic prompts, secrets and over-long queries before they reach the model.
Output scanners strip PII, secrets and unauthorized disclosures, block malicious links, and flag bias in generated responses.
Curated datasets and use-case-specific fine-tuned models reduce exposure to training-data poisoning and drift.
Strong access controls, network restrictions and auditing protect AI assets and their weights from exfiltration.
Retrieval-augmented generation ties responses to trusted enterprise sources instead of open model knowledge — reducing hallucination.
JSON schema enforcement constrains free-form generation, so an injected prompt can’t drive unsafe, unstructured behavior.
User-uploaded documents and embeddings live in private collections — no cross-tenant leakage, nothing shared with third parties.
For regulated estates, deploy fully isolated from external networks so data never leaves your perimeter.
Sensitive or binding actions require explicit human approval; low-confidence outputs escalate to a person, with configurable autonomy per use case.
Read more: Security & Compliance — the EU AI Act crosswalk → · Observatory →
Bring a workflow that touches PII and your compliance checklist. We'll show it running with redaction, grounding and output scanning — on your stack.