Cybersecurity for AI Systems
Secure Your Models & Data Pipelines
Security designed for AI: defending models and pipelines against modern adversarial threats while staying compliant by default across the whole supply chain.
AI breaks old security assumptions.
A traditional app does not get talked into leaking its database by a cleverly worded sentence, but an LLM can. A model can memorize and regurgitate training data, an agent can be tricked into misusing its own tools, and a poisoned dataset can compromise a system long before it ever reaches production.
Defense designed for how AI fails.
We test like an attacker before a real one does. Red-teaming probes your models and agents for jailbreaks, data-exfiltration paths, and prompt-injection weaknesses, and we harden what we find with guardrails, input sanitization, and tool-permission scoping so an agent can only ever do what it is explicitly allowed to.
Engineered to production standards.
- Defense against adversarial attacks and prompt injections
- AI red-teaming: jailbreak, data-exfiltration, and robustness testing
- GDPR, CCPA compliance via data privacy controls and zero-trust architectures
- Secrets management, SBOM supply-chain security, and policy-as-code
- Continuous monitoring and incident response for AI workloads
- Model & dataset provenance with signed artifacts, model registries, and audit-ready access logs
“AI systems your legal team can sign off on and your security team can monitor.”
Built for production from day one, then handed over with the runbooks so the capability stays with your team.
- 01Red-teaming an LLM application before it ships
- 02Defending agents and RAG pipelines against prompt injection
- 03Provable model and dataset provenance with signed artifacts
- 04GDPR/CCPA-ready data controls for AI workloads
Battle-tested tools, chosen to fit your team and constraints, never technology for its own sake.
- Zero-Trust
- AI Red-Teaming
- Policy-as-Code
- SBOM
- Model Registry
- GDPR / CCPA
Runs on
Your cloud account, your racks, or the edge.See the infrastructure behind it
Why teams pick us for this.
- 01
Security built for how AI fails
We defend against the threats traditional security misses, prompt injection, data exfiltration through models, poisoned data, not just the network perimeter.
- 02
We attack it before attackers do
Hands-on red-teaming (jailbreaks, exfiltration attempts, and robustness tests) finds the weaknesses while they are still cheap to fix.
- 03
Provable, audit-ready provenance
Signed artifacts, model registries, and access logs let you prove exactly which model and data are running, the evidence auditors and regulators ask for.
Answers before you ask.
Our security team is strong already. Why do we need AI-specific security?
Because AI introduces failure modes traditional controls don't address: prompt injection, model data leakage, and dataset poisoning. We complement your existing program with defenses built for those specific threats.
What does AI red-teaming actually involve?
We act as an adversary against your models and agents, attempting jailbreaks, data-exfiltration, and prompt-injection attacks and testing robustness, then report and help fix what we find before a real attacker does.
Can you help us stay compliant with GDPR and CCPA?
Yes. We implement data-privacy controls, zero-trust architecture, and policy-as-code so compliance is enforced by the system and backed by audit-ready logs, not dependent on manual process.
How do we prove which model and data are in production?
Through signed provenance, a model registry, and SBOM-style supply-chain tracking, so you have cryptographic evidence of exactly what is running and where it came from.
More of what we do.
Ready to build the future together?
Tell us where you're headed. We'll map the fastest secure path from idea to production.