Platform · Runtime
Run continuous AI threat monitoring for every client.
Ingest traces from AWS Bedrock, AWS SageMaker, Azure OpenAI, Google Vertex AI, Logfire, or LangChain. All live today, custom sources on request. Agents auto-discover. Threats surface to your SOC queue, tagged to the frameworks auditors ask about. Async observer, zero latency impact.
<1 HR
To first findings
6 SOURCES
Live, more on request
14 DAYS
Drift baseline window
ZERO MS
Added request latency
Why your clients need this
Your clients ship new AI features every sprint. Prompts change, models change, agents come and go. Auditors ask what is actually running in production today. Runtime answers continuously, without sitting in the request path.
Where your client's data flows
One path. Model-agnostic. Async observer.
Your client's app emits traces. We ingest from one of the six sources. Findings land in your SOC queue. That is it. We never sit in the request path, and we never care which model the client runs.
Client's AI app
Any model, any framework
Ingestion source
Bedrock · SageMaker · Azure · Vertex · Logfire · LangChain
EarlyCore Runtime
Async observer. Scan. Detect. Tag.
Your SOC queue
Slack · Teams · Email · Webhook · Jira
Model-agnostic
Claude, OpenAI, Gemini, Llama, Mistral, DeepSeek, custom fine-tunes. Whatever your client runs, if it flows through one of the six sources, we see it. Zero model-specific configuration.
Click through it
See a full Runtime session your analyst runs, start to finish.
Interactive walkthrough. Dashboard, live trace ingest, a real prompt-injection detection, triage, and export. About three minutes.
Every client, one console
Multi-tenant by design. Switch between clients from a dropdown. Findings tagged to the frameworks your client's auditor asks about. 15 minutes per client to connect, zero code changes.
Attack paths, Jira-ready
Full path traced from entry point to exploit. Auto-generated root cause. One click opens a ticket in your client's Jira or your SOC queue.
Alerts where your SOC works
Slack, Teams, email, webhook. No dashboard to babysit. Signal flows into the queue your analysts already run.
What Runtime watches for
Threats, drift, and every framework your client will be audited against.
Four threat classes, four drift types, seven frameworks. One console. No separate tool per category.
Threats
Inline detection
Prompt injection
Adversarial prompts designed to override agent behaviour
Data exfiltration
Secrets, credentials, or sensitive data leaving via responses
Sensitive data disclosure
PII, financial records, or health data surfacing in outputs
Secrets leakage
API keys, tokens, or passwords appearing in prompts or completions
Drift
Against 14-day baselines
Model drift
Agent model switches detected against a 14-day baseline
Prompt drift
System prompt modifications that change agent behaviour
Latency drift
Response-time anomalies against historical distribution
Error-rate drift
Spikes in failures, refusals, or hallucinations against baseline
Frameworks tagged
On every finding
MITRE ATLAS
Adversarial AI tactic taxonomy
OWASP LLM Top 10
Vulnerability classification per finding
EU AI Act
Article 15 robustness and cybersecurity evidence
GDPR
Article 32 security of processing evidence
DORA
ICT risk evidence for third-party AI operations
NIST AI RMF
Risk Management Framework alignment
ISO/IEC 42001
AI Management System evidence
Every issue and drift event carries clause-level tags, ready to export into an audit pack. Clause-by-clause mapping sits in the partner pack.
How you run it
Six steps from connection to client-branded report.
The flow your analyst follows, start to finish.
Connect a client data source.
Fifteen minutes. Pick one: AWS Bedrock cross-account IAM role, AWS SageMaker CloudWatch, Azure OpenAI connector, Google Vertex AI connector, a Logfire read token, or LangChain via OpenTelemetry. All live today. Custom sources on request. Zero code changes in the client's stack.
Agents auto-discover.
No manual registration. Each agent fingerprints from trace source, model, and system prompt. Deduplicated per tenant. First agents visible within the hour.
Detection runs continuously.
LLM Guard scanners for prompt injection, secrets, and sensitive data. Multi-stage verification kills false positives before findings hit your SOC queue.
Drift baselines build.
14-day rolling window per agent. Fifty traces minimum. Status surfaces as ACTIVE, INSUFFICIENT_DATA, or STALE, so your analyst knows when coverage is real.
Issues land in your queue.
Slack, Teams, email, webhook, or direct Jira ticket creation. One-click suppression rules per client so recurring false positives do not come back.
Export a client-branded report.
Your logo, your colours, your analyst name. Share link with password and expiry, PDF, or API handoff into your client's ticketing system.
The partnership
What you keep. What we handle.
Channel-only, written into the partner contract. Your clients never hear our name unless you mention it. You carry the relationship. We carry the engine. Neither of us does the other's job.
You keep
- Client relationship
- First-line triage and SOC workflow
- Report branding and retention policy
- Retainer revenue (typically 3× traditional MSSP resale)
- Per-client suppression rules and alert routing
We handle
- Trace ingestion and agent auto-discovery
- LLM Guard scanners and verification pipeline
- Framework-clause mapping on every finding
- Platform hosting (OVHcloud in France, Cloud Act exempt)
- Zero-retention mode for regulated clients
Connect a client endpoint in a 30-minute call.
We wire a client data source to EarlyCore, watch the first findings land in your SOC queue, and hand over a branded report template. European MSSPs, no commitment, NDA on request.