Security and trust
Security and trust, by design.
Enterprise data lives in regulated environments. Fligoo's security posture is engineered to fit those environments — not retrofitted into them. Every layer, from access to inference, is built so the audit trail answers the questions a compliance, risk, or M&A diligence team will ask.
A four-stage security stack across the implementation.
Security is sequenced into the deployment, not bolted on at the end. Each stage is observable, auditable, and aligned with the client's existing policy framework.
01
Stage
Security layer setup
VPN tunnels or secure connection gateways into the client environment; end-to-end encryption in transit and at rest; role-based access (RBAC); SOC 2, GDPR, and client-specific policy alignment.
02
Stage
Data access and integration
Multiple secure integration paths — API and MCP servers, structured SFTP file uploads, read-only direct database access, and cloud bucket integration via IAM and signed URLs. Schema mapping is automated where possible.
03
Stage
Data selection
Pre-defined data maps for financial services with built-in prioritization by use case. Clients don't modify their systems — we adapt to them. Auto-discovery tools surface relevant signals across client profile, account-level, engagement, product usage, and behavioral data.
04
Stage
Data movement and processing
Modular ingestion framework with plug-and-play connectors (Salesforce, Snowflake, Redshift, BigQuery, S3, Azure Blob, GCP), event-driven processing, and discard rules — minimal client-side change required.
Authentication and access
Identity, MFA, and role-based access enforced everywhere.
JWT-based authentication
Stateless, tamper-evident tokens scoped per service and per role.
MFA via TOTP
Time-based one-time passwords required for human access to sensitive surfaces.
Role-Based Access Control (RBAC)
Permissions modeled per role and scoped to the data, models, and tools each role legitimately needs.
Profile-scoped query access
Database query profiles narrow the table scope per role — analysts, ML engineers, and operators each see only what their work requires.
End-to-end encryption and a hardened perimeter.
Data is encrypted at rest and in transit, moved over VPN or signed-URL channels, and never replicated outside the agreed perimeter.
In transit
TLS for API and direct database channels; signed URLs for cloud bucket transfer; VPN tunnels for direct connection paths.
At rest
Cloud-native encryption on data lake, model artifacts, and audit logs — keys managed in the client's KMS where required.
Perimeter
Read-only access wherever possible; no write-back into source systems unless the use case requires it and the client approves.
Compliance
SOC 2, GDPR, and client-specific policies.
SOC 2
Operating practices aligned with SOC 2 controls across security, availability, processing integrity, confidentiality, and privacy.
GDPR
Data minimization, purpose limitation, data subject rights, and processor-controller boundaries supported in design.
Client policy alignment
Engagements are scoped to honor each client's information-security policies, data-residency rules, and audit requirements.
AI guardrails
Reliability and safety enforced across the model lifecycle.
The same four-layer guardrail framework documented on the platform page applies to every deployment — data, model, output, and system.
01
Data
Schema validation, drift detection, PII filtering, and sensitive-attribute checks before data enters training or inference.
02
Model
Fairness adjustments, differential-privacy noise where applicable, and stability checks that flag instability before it reaches production.
03
Output
Toxicity and policy compliance checks on every prediction; explainability metadata attached to every score.
04
System
Policy engine, audit log, retraining SLA, and rollback readiness — recovery is a known procedure, not an improvisation.
Operational guardrails
Constraints applied at the database and inference boundary.
Generative AI surfaces — including any natural-language access to enterprise data — operate inside explicit, layered limits.
Two-stage SQL execution
Show-then-run query pattern: queries are validated and explained before they are executed.
Result limiting
Result sets are capped at the application layer (FETCH FIRST 1000 ROWS ONLY) regardless of query intent.
Query complexity bounds
Joins capped at two; SELECT * disallowed; WHERE clauses required; index-aware execution preferred.
LLM-as-a-Judge complexity preview
EXPLAINSQL pattern lets the model evaluate query complexity and likely performance impact before running.
Syntactic validation
Every generated query is parsed and validated against the allowed grammar before reaching the database engine.
Aggressive timeouts
Short query timeouts ensure runaway operations are interrupted long before they affect production workloads.
Auditability and lineage
Full traceability — from data ingestion to model recommendation.
Data lineage
Catalog and version of every dataset entering the pipeline, with quality and discard metrics logged.
Model traceability
MLflow + model registry + Bitbucket — every training run, dataset hash, and feature configuration is reproducible.
Prediction-level rationale
Top drivers and counterfactuals attached to every recommendation, so audit teams can answer "why".
Bias auditing
Fairness audits on protected attributes with results logged alongside the model card.
Data residency
Federated learning where data cannot leave the perimeter.
When regulation, partnership constraints, or risk tolerance forbid data movement, foundational models can be trained federatedly. Only learnings — never raw data, never features — cross the boundary.
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