ML security systems
Behavioural baselines, anomaly scoring, peer context, model evaluation, and threshold tuning.
Security AI systems
Security AI built for analyst review.
A model score needs context, evidence, and a review path.
Vyshyvka builds the data pipelines, review screens, and reports around the model so analysts can inspect the evidence and decide what happens next.
What this covers
- Detection engineering
- AI/ML security
- LLM triage
- Security data platforms
- SIEM tooling
- Sensitive workflows
- Analyst workflows
- Evidence review
- Audit trails
- Edge deployment
Operating range
From telemetry ingestion to analyst disposition.
Experience in sensitive environments covers behavioural analytics, LLM-assisted triage, analyst tooling, and investigation reporting. Each implementation preserves the source events, scoring context, analyst decision, and final report.
- Production
- security engineering in live environments
- Detection
- model-assisted scoring and triage
- Analysts
- queues, cases, evidence, and review paths
- Reporting
- clear findings with caveats and source context
- Delivery
- typed contracts, tests, deployment, and operations
- End-to-end
- data, models, tools, response, and evidence
Requirements
Analyst workflow requirements.
A useful tool starts with a defined security problem and usable data. It constrains model output, provides a review path, and produces reports that analysts can challenge.
LLM detection workflows
Structured triage, drift checks, evidence handling, escalation paths, and cost-aware gating.
Security data platforms
Telemetry modelling, signal persistence, migrations, ingestion boundaries, and reviewable records.
Analyst product design
Triage queues, case views, dashboards, evidence tables, analyst actions, and audit trails.
Operations and infrastructure
Authentication-aware deployment, containerized services, cloud infrastructure, search, health checks, and jobs.
Investigation advisory
Security reporting with clear methods, caveats, evidence summaries, and decision-ready findings.
Relevant experience
Detection, triage, and investigation experience.
The work includes event schemas, scoring services, analyst queues, evidence views, audit records, and written findings with stated caveats.
Signal definitions, model-assisted scoring, analyst review states, feedback paths, and dashboard views.
Staged review logic, structured model outputs, persistence, quality gates, and human escalation.
Queue state, case escalation, evidence tables, dashboard views, and analyst actions.
Role-aware controls, audit logging, unstructured log parsing, weighted review logic, and exception handling.
Model training, statistical scoring, experimental graph work, searchable outputs, and evaluation loops.
Multi-source analysis, caveated findings, review-ready summaries, and clear methods.
Narrative Security
Narrative scores behavioural sequences for analyst review.
Narrative models user behaviour as event sequences, scores unusual activity with user and peer context, and presents the result in a SOC-style dashboard for review.
Security analytics platform
Narrative Security Operations
Prototype work covering behavioural sequence scoring, Cloudflare-based runtime architecture, SOC dashboard screens, alert review, user context, red-team review, and model explanation.
View case study- 29M
- parameter transformer
- 144
- behaviour tokens
- 22MB
- INT8 model artifact
- 27
- Next.js page routes
- 5-stage
- ML scoring pipeline
- 6
- Cloudflare runtime primitives
Review requirements
Evidence and model context in the review screen.
Before a tool can trigger an action, analysts need to see the source events, the model's inference, and the decision that still requires human review.
Evidence first
Signals, model outputs, and findings include source references, caveats, and the context an analyst needs for review.
Typed handoffs
Contracts keep service outputs, queue payloads, filters, and analyst actions explicit.
Model lifecycle
The delivery plan covers training, evaluation, quantization, artifact storage, and active-version switching.
Analyst workflow
Analyst tools need queues, timelines, peer context, review screens, data exploration, and audit records.
First discussion
Bring a representative alert or investigation.
Include the telemetry available, who reviews it, what decision follows, and which errors would be costly.
Architecture review
Walk through the event model, scoring path, deployment shape, UI surfaces, and tradeoffs.
Problem-fit discussion
Map the real security, AI, or data problem to systems that have been built and operated.
Code-oriented review
Discuss contracts, validation, artifacts, workflow boundaries, reliability, and implementation choices.
Case-based exercise
Use noisy telemetry, ambiguous behaviour, stakeholder constraints, and evidence requirements.
Project fit
Suitable security AI projects.
Good fit
- Security tools where model output has to be reviewed before anyone acts on it.
- Detection engineering tools with messy multi-source data.
- Security data products that need validation, auditability, and analyst trust.
- LLM triage where cost, scope drift, and evidence quality matter.
- Sensitive work where analysts must trace a finding back to its evidence.
Not a fit
- Generic chatbot apps with no security workflow.
- Compliance checkbox tooling with no evidence model.
- Dashboards that only restyle existing alerts.
- AI strategy without implementation responsibility.
- Projects without enough source material to model the work.
Start the work
Describe the alert, the evidence, and who decides what happens next.
Include the available telemetry, current triage process, and the action an analyst may take after review.
Send a project brief