Cybersecurity for AI Systems
“Secure Your Models & Data Pipelines”
AI systems fail in ways traditional security never had to consider, prompt injection, data exfiltration through a model, poisoned training data. We secure the whole pipeline, from adversarial red-teaming to signed model provenance and zero-trust controls, so your AI stays compliant and resilient by default. Every model, dataset, and access path is accounted for and audit-ready.
AI breaks old security assumptions.
AI systems break security assumptions that held for decades. 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.
Most security programs were not built for any of this. Firewalls and access controls still matter, but they don't catch prompt injection, they don't prove a model is the one you trained, and they don't tell you whether your AI is quietly exfiltrating data through its outputs. Securing AI takes controls designed for how AI actually fails.
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.
Around that we build the controls that make AI trustworthy and auditable: signed model and dataset provenance so you can prove what is running, a model registry and SBOM for supply-chain integrity, and zero-trust architecture with policy-as-code so compliance, GDPR and CCPA included, is enforced by the system rather than left to good intentions.
Everything you need, engineered to production standards.
Security designed for AI: defending models and pipelines against modern adversarial threats while staying compliant by default across the whole supply chain.
- 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.”
We engineer for production from day one, then transfer ownership so the capability stays with your team.
Built for the problems you're actually facing.
- 01 Red-teaming an LLM application before it ships
- 02 Defending agents and RAG pipelines against prompt injection
- 03 Provable model and dataset provenance with signed artifacts
- 04 GDPR/CCPA-ready data controls for AI workloads
The stack we reach for.
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
Why teams pick AzeniQ for this.
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.
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.
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.
Engagements designed to leave you stronger.
Every service follows the same disciplined path: de-risk fast, engineer for production, then transfer ownership.
Frame & de-risk
We pressure-test the goal, define measurable outcomes, and ship a focused proof-of-concept fast.
Engineer to production
Hardened, observable, cost-aware systems built on AWS/Azure/GCP with security by default.
Transfer & scale
We embed the practices and mentor your team so the capability stays in-house.
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, 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.