Security and economics for AI systems
Built for CISOs. Adopted by builders.
One platform for AI security outcomes.
Triage secures LLM-powered products across inference, retrieval, and training workflows. It works as a security control for traditional environments or as a daily engineering tool for teams shipping AI features. Same data, same controls, different ownership models.
Not a single "team product." A security system that adapts to how you run.
AI changes the shape of the attack surface. The failure modes are not confined to code. They include prompts, tool calls, retrieval chains, data curation, evaluation harnesses, and model routing.
Triage is intentionally malleable: it can be owned by a security organization, embedded into a platform team, or used directly by the engineers building AI systems.
Centralized governance when you need policy, auditability, and control.
Developer-native workflows when you need speed, iteration, and coverage.
A shared source of truth so security and engineering stop arguing about what happened.
For traditional security teams
Centralized AI telemetry, investigations, and policy enforcement
Runtime guardrails for tool use, data access, and exfiltration patterns
Audit-ready evidence across inference, retrieval, and training pipelines
For startups and product teams
Trace-driven debugging of agents and RAG systems
Regression detection for prompts, retrieval, and routing
Cost controls across tokens, latency, retries, and provider usage
Choose your operating model
Deploy Triage to fit your org structure, not the other way around.
Security-owned deployment
A security team owns policy and risk posture, and uses Triage to monitor AI execution paths, investigate incidents, and enforce controls across products.
What changes for you
Opaque model behavior becomes inspectable traces
Incident response time drops dramatically
Controls scale without multiplying headcount
Typical outputs
Policy gates for prompt/tool behavior
Centralized investigations with evidence
Security reporting on runtime events
Engineering-owned deployment
Engineers use Triage as part of shipping. They instrument AI features, observe failures and regressions, and remediate issues before they become incidents.
What changes for you
Failures become reproducible, not anecdotal
Regressions get caught before shipping
Wasted spend on retries disappears
Typical outputs
Debuggable traces for agent behavior
Guardrails in CI/CD for prompt changes
Lower latency and cost via tuning
Shared deployment
Security defines the policy and severity model. Engineering owns uptime, quality, and velocity. Triage becomes the shared runtime layer.
What changes for you
Fewer handoffs, fewer "can't reproduce" loops
Controls that ship with code, not against it
One trace format for audit and incident response
Typical outputs
Unified governance and velocity
Shared visibility into risk and reliability
Faster feedback loops for all teams
Security that pays for itself
AI systems incur costs in places most teams do not measure: inference waste, retrieval noise, tool failures, latent prompt regressions, and incident response time. Triage makes these measurable and controllable.
Risk-adjusted loss
Fewer successful exploits, smaller blast radius, higher confidence in control effectiveness.
Engineering time
Faster debugging, fewer escalations, fewer recurring failure patterns.
Compute spend
Fewer retries, better routing, lower token waste, reduced provider churn.
Support and uptime
Fewer "AI did something weird" tickets, less downtime, fewer rollbacks.
Compliance overhead
Evidence-quality telemetry for audits and reviews, without manual log stitching.
Who uses Triage
Own the risk posture
CISO / Security Leadership
Own policy, reporting, and risk posture for AI products.
Get evidence, not opinions: what the model saw, what it did, what tools ran.
Prove controls work at runtime.
Make AI testable
AppSec / Product Security
Turn AI behavior into enforceable, testable rules.
Catch prompt and retrieval regressions before they ship.
Reduce "unknown unknowns" in agent workflows.
Standardize instrumentation
Platform / Infrastructure
Standardize instrumentation and guardrails across teams.
Reduce cost variance, timeouts, and provider failure modes.
Monitor reliability across model providers and tool chains.
Debug with context
AI Engineers
Debug agent behavior with full execution context.
Validate retrieval quality and prevent data leakage.
Close the loop between eval failures and production traces.
Adoption that matches your constraints
Some teams start with governance. Others start with engineering pain. Triage supports both entry points.
1. Define policies and severity thresholds
2. Instrument critical systems
3. Expand coverage with standard controls
1. Instrument one high-traffic AI workflow
2. Use traces to remove latency/failure hotspots
3. Add guardrails once you have observability
Deploy it as a security product or an engineering system.
The outcome is the same: Lower AI risk. Lower operating cost. Faster iteration.
Talk to usIs Triage for security teams or engineering teams?
Both. The platform is designed so ownership can sit with security, engineering, or a shared model without duplicating tooling.
Does this slow teams down?
The goal is the opposite: make AI failures reproducible and prevent regressions early, so teams spend less time firefighting.
Where does the economic value actually come from?
From measurable reductions in incident cost, engineering time, compute waste, and quality regressions that spill into support and churn.