AI Continua / AI Security
Secure the AI you already run, and the AI you are about to ship
Language models, retrieval pipelines and agents create attack surface that firewalls and code scanners do not see. We find it, close it, and keep watching it.
The risks
What goes wrong in AI systems, and what we do about it
Categories follow the OWASP Top 10 for LLM Applications and MITRE ATLAS, so your team can trace each control back to a recognized reference.
| Risk | How it shows up | How we address it |
|---|---|---|
| Prompt injection | A customer email, web page or shared document contains hidden instructions. The assistant follows them instead of yours. | Input and content isolation, instruction hierarchy, output validation, and an attack suite replayed on every release. |
| Sensitive data disclosure | The model repeats personal data, credentials or confidential documents from its context, retrieval index or training data. | Data classification before indexing, per-user retrieval permissions, output filtering and redaction, log hygiene. |
| Excessive agency | An agent with broad API or database access takes a destructive action after a manipulated instruction or a plain mistake. | Scoped tool permissions, human approval for sensitive actions, rate and spend limits, full action logging. |
| Insecure output handling | Model output is passed unchecked into a browser, shell, SQL query or downstream service. | Treat model output as untrusted input: encoding, schema validation, sandboxed execution. |
| Supply chain | A downloaded model, plugin, dataset or MCP server carries malicious code or poisoned content. | Provenance checks, model and dependency inventory, pinned versions, isolated runtime for third-party tools. |
| Data and model poisoning | Tampered fine-tuning data or knowledge-base content quietly changes model behavior. | Controlled ingestion paths, content review gates, integrity checks, behavioral regression tests. |
| Abuse and cost exhaustion | Automated misuse, model extraction attempts or runaway usage drive up spend or degrade service. | Authentication, quotas, anomaly detection, abuse monitoring, circuit breakers. |
Service lines
Pick one, or combine them
AI threat modeling
Map each AI system's data flows, trust boundaries, tools and users, then rank the realistic attack paths. Best before build or before a major change.
Guardrails and secure architecture
Design and implement input and output controls, permission models, retrieval access rules and approval flows that fit your stack and latency budget.
Red teaming
Human-led adversarial testing of your chatbots, copilots and agents, using attack techniques from current research and real incidents.
Monitoring and detection
Logging, drift checks and alerting for prompt abuse, data exposure and unusual agent behavior, connected to your SIEM and incident process.
AI governance and policy
Acceptable-use policy, AI system inventory, risk classification and review workflows mapped to NIST AI RMF, ISO/IEC 42001 and the EU AI Act.
Third-party AI risk review
Assess vendors and AI features inside tools you buy: what data they see, where it goes, what they retain, and what contract terms you need.
Defense in depth
No single guardrail is enough, so we layer them
Filters get bypassed. Models change behavior between versions. Reliable protection comes from several independent controls that each limit the damage if another fails.
- Before the model: authentication, input screening, content isolation, retrieval permissions.
- Around the model: system prompt hardening, tool allow-lists, sandboxing, approval gates.
- After the model: output validation, redaction, rate limits, action logging.
- Over time: regression tests, drift monitoring and scheduled re-assessment.
What you receive
Deliverables your team can use the next day
System inventory
Every AI system, model, dataset, vendor and owner in one register.
Threat models
Ranked attack paths for each system, with the assumptions written down.
Implemented controls
Working guardrails and configurations, not only recommendations.
Regression suite
Attack tests that run in your CI pipeline on every change.
AI security questions
We only use a vendor's AI features. Do we still need this?
Usually yes. Even when you do not host a model, you control what data the feature can see, who can use it and what it can do. A third-party AI risk review covers exactly that.
Can guardrails fully stop prompt injection?
No control eliminates it today. The goal is to limit what a successful injection can do: narrow permissions, approval steps and monitoring. We are direct about residual risk in every report.
Do you work with our existing security tools?
Yes. We integrate with your logging, SIEM, CI/CD and ticketing so AI findings follow the same workflow as everything else.
Can you help us prepare for the EU AI Act or ISO/IEC 42001?
We can help you inventory and classify systems, build required documentation and close technical control gaps. Legal interpretation should come from your counsel.
Start with a scoping call
Tell us which AI systems you run. We will tell you where we would look first.