SOLUTIONS — BY ROLE: HEAD OF AI
Release Faster.
Reduce Rework. Prove Safety.
TestSavant.ai combines Red Teaming and the Nero attacker to surface real failure modes, then uses Auto-tune (Airflow) to retrain and redeploy guardrail models via the TestSavant API on Ray. The Data Synthesizer & Aggregator keeps datasets clean and current.
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Fewer Critical Findings
Real failure modes discovered pre-prod via hybrid Red Teaming + Nero.
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Faster Release Cycles
Auto-tune automates retrain→validate→redeploy; diffs and metrics stored.
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Better Data Quality
Synthesizer & Aggregator fuses telemetry, synthetics, and domain corpora.
Build a Safety-First AI Delivery Loop
Keep models improving while shipping on schedule.
Evaluation & Red Teaming
- Attack libraries (injection, exfiltration, tool abuse)
- Nero-generated novel samples; black-box model support
- Coverage metrics; fail gates on critical regressions
Auto-tune Retraining
- Airflow pipeline: ingest → retrain → validate → redeploy
- Human approval optional; rollbacks and diffs recorded
- Rapid cadence to keep guardrails current
Runtime Guardrails (API on Ray)
- Low-overhead checks for prompts, RAG, and tools
- Categories: prompt-injection, toxicity, privacy/PII, tool-safety
- Telemetry feeds Aggregator for the next cycle
Data Synthesizer & Aggregator
- Fuse traces, Nero, Red Teaming, synthetics, domain corpora
- Produce clean datasets for training & evaluation
- Support for external sources (papers, HF, CSV)
From Failures to Better Models
Turn every finding into training signal and runtime protection.
| Failure Mode | Runtime Guardrail | Test Method | Result |
|---|---|---|---|
| Prompt injection / jailbreak | Block/transform; quarantine | Nero + Red Teaming lure suites | Takeover blocked; logs |
| Weak citations / RAG hallucination | Deny without strong provenance | Source-integrity checks | Trustworthy answers; lineage |
| PII/PHI exfiltration | Detect → mask/tokenize | Adversarial PII payloads | Lower leakage; proofs |
| Tool misuse | Deny/transform risky calls | Function-call abuse suites | No unsafe actions; audit trail |
| Drift / robustness regressions | Auto-tune retrain & redeploy | Scheduled regression packs | Controlled updates; diffs |
Architecture & Controls
Eval → synthesize → retrain → redeploy → observe. Repeat.
Deployed Guardrail Models
- API on Ray; categories: injection, toxicity, privacy/PII, tool-safety
- Telemetry per request for training loops
Red Teaming (Hybrid)
- Automated + manual; black-box friendly
- Updated from Nero & research
Auto-tune (Airflow)
- Ingest → retrain → validate → redeploy
- Diffs & metrics archived
Nero (Autonomous Attacker)
- Self-play; learns from traces
- Feeds successful samples to Red Teaming
Attack Knowledge DB
- Patterns/signatures + examples
- Retrieval memory for Nero
Data Synthesizer & Aggregator
- Fuse telemetry, synthetics, domain sets
- Produce clean datasets for training/tests
Evidence Support for AI Frameworks
Artifacts aligned to ISO/IEC 42001, ISO/IEC 23894, NIST AI RMF, GDPR Art 22/15(1)(h).
NIST AI RMF 1.0
- ✓Risk registers from findings, drift, and performance metrics.
ISO/IEC 42001
- ✓PDCA artifacts: diffs, validations, incident learnings.
ISO/IEC 23894
- ✓Evidence of risk lifecycle and re-tests.
GDPR
- ✓Explainability excerpts; human-review trails where needed.
Frequently Asked Questions
How do we plug this into CI/CD?
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Run Red Teaming suites as a gate. On critical findings, block release; Auto-tune can retrain guardrails and re-run evaluation before go-live.
Do you support closed provider APIs?
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Yes—black-box probing via prompts/files/tools; runtime guardrails enforce policy at the orchestration edge.
Ship More Safely—With Less Rework
See how evaluation findings flow into retraining and back into runtime guardrails via the TestSavant API on Ray.
