AI for security teams — and security for the AI you ship
AI for security means applying machine learning and large language models to security operations — alert triage, threat analysis, incident response and reporting — while making sure your own AI systems can't leak data or be manipulated. Sysvoy has built AI-driven security platforms for a European cybersecurity company, and applies the same discipline to every LLM product we ship: threat modelling, prompt-injection defences, least-privilege access and full audit trails.
Where AI for security projects stall
And what we do differently.
Security bolted on later
Controls added after launch leave gaps. We threat-model before the first line of code and design controls for each risk.
An LLM trusted with everything
A model that can read everything can leak everything. We scope data and tools per user and per task.
Black-box alerts
Analysts won't act on triage they can't inspect. Our tools show the evidence behind every recommendation.
What we build
SOC copilots
Alert triage, enrichment and plain-language incident summaries that cut the time analysts spend on low-value alerts.
Incident response platforms
Case management, playbooks and timelines that keep responders, evidence and decisions in one place.
Cyber exercise and training tools
Interfaces for running and reviewing security exercises, from scenario design to after-action reports.
Asset and threat visibility
Unified views of IT and OT assets and their risk, designed with the engineers who run those environments.
Hardened LLM applications
Prompt-injection defences, output filtering, PII redaction and audit logs for any AI feature that touches sensitive data.
How we deliver
From the first workshop to production. How we work
Threat model first
Before building, we map what the system can access, who might attack it and how, and design controls for each path.
Least privilege everywhere
Models and agents get the narrowest permissions that work, with secrets kept out of prompts and logs.
Adversarial testing
We attack our own systems before launch — injection, data exfiltration, jailbreaks — and fix what we find.
Your environment, your keys
Deployments run in your cloud or on-premise, with self-hosted models when data can't leave the building.
Cloud model APIs compared with self-hosted models for security work
| Cloud model API | Self-hosted model | |
|---|---|---|
| Data leaves your environment | Yes, under the provider's terms | No |
| Model capability | Strongest frontier models | Good and improving; depends on hardware |
| Running cost | Pay per use | Fixed infrastructure |
| Best for | Data covered by contract and policy | Regulated or highly sensitive data |
A good fit when
- Your analysts are buried in alerts and need triage that explains itself.
- You sell a security product that needs AI features without new attack surface.
- Compliance requires AI that runs inside your own environment, with a full audit trail.
What we build with
- Python
- TypeScript
- Node.js
- Self-hosted and commercial LLMs
- SIEM and ticketing integrations
- On-premise deployment
- AWS
- Google Cloud
Related work
AI security platform
AI-driven security platform and SOC tooling for a European cybersecurity company.
Client under NDARegistry in the cloud
Moved a Canadian registry's platform to the cloud, and keep extending it as their needs grow.
Ongoing engagementOtterSign
E-signature platform for a client's waiver and document workflow business.
ottersign.com ↗
Common questions
Not answered here? Email contact@sysvoy.com.
Can you deploy on-premise?
Yes. We deploy into your own data centre or private cloud and use self-hosted models where data can't leave your environment. Fully air-gapped setups need offline model updates, which we scope case by case.
How do you protect LLM applications against prompt injection?
With layers: separating trusted instructions from untrusted content, scoping tool permissions, filtering outputs, requiring human approval for sensitive actions, and testing with injection attacks before launch. No single filter is enough on its own.
Do you work with OT and industrial environments?
Yes. We have designed unified OT asset visibility and management tooling, and we work alongside your OT engineers on anything that touches control networks.
Can you modernise an existing security platform?
Yes. We can rebuild the interface and architecture of an existing platform while keeping its data and workflows intact, so your users don't lose what already works.
Other services
- AI agent developmentProduction AI agents and multi-agent systems that take research, triage, document review and operations off your team.
- LLM & RAG developmentRetrieval pipelines grounded in your own data, with evaluation and citations so answers can be checked, not just trusted.
- SaaS product developmentFull-stack Next.js, Node and TypeScript products — web and mobile — from a validated MVP to a platform that scales.
- Integrations & cloud migrationSalesforce and HubSpot integrations, legacy systems connected to modern ones, and applications moved to the cloud.
- Dedicated development teamsSenior engineers from our Lahore studio who work inside your tools and rituals, with US-based staff for onsite alignment.
Have something to build?
Tell us where things stand and what you need. A short message is enough to start.