AI in Cybersecurity
Alert triage and evidence assembly, without widening the attack surface.
Security teams have an alert volume problem and an evidence problem, and language models are genuinely good at both. They also have an unusually sharp reason to care where the model runs, since the telemetry is a map of the environment and the vendor questionnaire the team sends everyone else applies to them too.
Book a free consultationWhat makes this hard
- Alert volume exceeds analyst capacity, so triage quality degrades exactly when volume spikes
- Tier-one analysts spend their time on context assembly rather than judgment
- Control evidence for audits is gathered manually, repeatedly, from the same systems
- Security telemetry sent to a third-party model is a detailed map of your environment
Where AI earns its place
Alert Triage and Enrichment
Assemble the context an analyst would gather manually (asset, owner, recent changes, related alerts) and present a reasoned summary with the evidence attached.
Analyst time on decisions, not lookupsDetection Engineering Support
Draft and document detection logic from threat intelligence, and surface coverage gaps against your own environment rather than a generic framework.
Coverage gaps made visibleControl Evidence Assembly
Collect and structure evidence for SOC 2, ISO 27001 and internal audit from the systems that already hold it.
Audit preparation without a fire drillPhishing and Report Triage
Classify user-reported email at volume, escalating genuine campaigns immediately and closing the rest with an explanation the reporter can read.
Same-hour response on user reportsFigures are drawn from Senteras engagements and are illustrative of typical results. Outcomes vary by data quality, infrastructure and scope.
The rules that shape the build
These are the constraints that decide the architecture, usually before anyone has picked a model. This is general information about the regulatory landscape, not legal advice on your obligations.
Your own vendor risk program
Security teams apply the strictest third-party standards in most organizations. Sending telemetry to an external model means answering the questionnaire you send everyone else, and self-hosting is often the shorter path.
Incident notification timelines
SEC, state and sector rules impose short notification windows. AI-assisted triage has to demonstrably speed detection rather than introduce an unexplainable step into the timeline.
Evidence integrity
Where output may support an investigation or a claim, chain of custody and reproducibility matter more than convenience.
The model never closes an alert
Senteras builds security AI that summarizes, enriches and ranks, and never auto-closes. A model that closes alerts will eventually close the one that mattered, and the failure is silent by construction: nobody reviews what was dismissed. Analyst time is saved by removing the lookup work before the decision, not by removing the decision.
Where this applies
The same core systems, with the differences that matter in each setting.
- Internal security operations
- Triage enrichment carries the clearest return, measured in analyst hours per shift.
- Managed security providers
- Same work across many tenants, where isolation in the retrieval layer is the hard engineering problem.
- GRC and compliance teams
- Evidence assembly and control narrative drafting.
- Incident response consultancies
- Timeline construction from log and artefact review under time pressure.
Common questions
Will you auto-close alerts?
No. A model that closes alerts will eventually close the one that mattered, and the failure is silent because nobody reviews what was dismissed. We remove the lookup work before the decision, not the decision.
Is sending telemetry to a cloud model a risk?
Your telemetry is a detailed map of your environment, and sending it out means answering the same vendor questionnaire you send everyone else. Most security teams find self-hosting the shorter path, which is unusual candour from a firm that could sell either.
Can it write detections?
It drafts and documents detection logic from threat intelligence and surfaces coverage gaps against your actual environment. A detection engineer reviews and tunes, because a detection that fires wrongly at scale is its own incident.
Does this help with SOC 2 or ISO 27001?
Evidence assembly is one of the clearest wins. The systems already hold the evidence; collecting and structuring it is repetitive work that recurs every audit cycle and gets done under time pressure.
How we build it
Local & On-Prem LLM Deployment
The most powerful AI models, running entirely on your hardware.
Custom AI Agents & Automation
AI that doesn't just answer questions. It gets things done.
Model Fine-Tuning & Integration
Models that speak your industry's language, trained on your data.
AI Data Privacy
Keeping regulated data out of models you do not control.
Internal Knowledge Base
Answers from your own documents, under your existing permissions.
Fraud Detection
Fewer false positives, and a reason attached to every flag.
Start with a conversation, not a proposal
Thirty minutes. We will tell you what we would change first, and whether you need us at all.
Book a callThe firm behind the firm