Flagship practice
Secure AI: because your models now read your data and act on it
AI moved from pilot to production faster than any control framework could follow. Blackharbor treats the model, the retrieval pipeline and the agent as one attack surface — then tests it the way an attacker would.
Four layers
Securing AI is four jobs, not one procurement checkbox
01
Secure the model
Adversarial testing across prompt injection, jailbreaks, tool abuse, model extraction and training-data poisoning — with fixes, not just findings.
02
Secure the pipeline
Retrieval sources, embeddings, vector stores, fine-tuning data and model supply chain reviewed for tenancy leakage and provenance gaps.
03
Secure the agent
Least-privilege tool access, human-in-the-loop boundaries, sandboxed execution and audit trails for every autonomous action taken.
04
Govern the programme
Use-case inventory, risk classification, approval gates and continuous evaluation mapped to the NIST AI RMF and ISO/IEC 42001.
Threat model
What actually goes wrong
Prompt injection & tool abuse
Untrusted content reaching a model with privileges turns retrieved text into instructions. We test every path where data becomes an action.
Sensitive data leakage
Context windows, logs, caches and embeddings all become copies of your regulated data. We trace where it lands and who can reach it.
Model & supply-chain integrity
Third-party weights, plugins and packages carry unverified provenance. We review integrity controls before they reach production.
Unbounded autonomy
Agents fail in ways a reviewer never scripted. We define blast radius, approval gates and rollback before you widen their scope.
What an engagement includes
Governance, threat modelling and adversarial testing for the models, agents and pipelines now making decisions inside your business.
We deliver findings your engineers can act on in the same sprint, plus the governance artefacts your risk, legal and customer-diligence teams need.
Talk to Blackharbor- AI/LLM threat modelling: prompt injection, tool abuse, data exfiltration
- Adversarial evaluation of models, agents and retrieval pipelines
- Guardrail, evaluation and monitoring architecture
- AI governance mapped to the NIST AI RMF and ISO/IEC 42001
- Training-data provenance, tenancy and model supply-chain review