Building an evidence-grounded agentic security operations harness on Cloudflare

Building an Evidence‑Grounded Agentic Security Operations Harness on Cloudflare

Security teams are constantly battling an avalanche of alerts. When a single alert spikes, analysts scramble to understand its context; when dozens arrive simultaneously, the workload can become unmanageable. Cloudflare’s latest offering—an integrated, multi‑AI‑agent security operations harness—addresses this “alert paradox” by automating evidence gathering, correlating detections, and surfacing actionable insights at scale.

From Alert Overload to Automated Context

Traditional SIEMs excel at ingesting data but still rely heavily on human analysts to:

• Identify which alerts are related.
• Fill gaps where telemetry is missing.
• Decide whether an alert is a false positive, true positive, or requires escalation.

The new harness replaces much of this manual triage with a coordinated fleet of AI agents. Each agent specializes in a stage of the workflow:

1. Data Harvesting – Pulls logs, network flows, and endpoint telemetry from Cloudflare’s edge and core services.
2. Detection Aggregation – Maps raw alerts to known threat patterns, linking related events across domains, IPs, and user accounts.
3. Evidence Gap Analysis – Flags missing data sources and suggests supplemental collection actions.
4. Decision Scoring – Uses Cloudflare’s open‑source Clef decision model to assign confidence scores to each alert.

By the time a human analyst reviews the case, the harness has already produced a concise, evidence‑backed summary.

Leveraging Trusted Large‑Model Partnerships

Cloudflare integrates approved large‑language models (LLMs) from OpenAI’s Daybreak Defense Network and Anthropic. These models—such as the GPT‑5.6 Cyber and Mythos series—provide deep, model‑backed analysis without exposing proprietary data. Their role is twofold:

• Contextual Enrichment – The LLMs interpret raw logs, translate technical jargon, and surface relevant threat intelligence.
• Recommendation Generation – Based on the aggregated evidence, the models suggest next‑step actions (e.g., quarantine, ticket creation, or escalation).

All model interactions are sandboxed and audited to meet Cloudflare’s security and compliance standards.

A Practical Workflow for Managed Defense Analysts

1. Alert Ingestion – An incoming alert triggers the harness. The agents immediately begin data collection across Cloudflare’s edge, DNS, WAF, and network telemetry.
2. Evidence Assembly – Within seconds, the system presents a unified view: related alerts, supporting logs, and identified gaps.
3. Scoring & Prioritization – Clef assigns a risk score, while the LLMs add a confidence rating for each suggested action.
4. Analyst Review – The analyst sees a single dashboard with:

- Linked alerts and their timelines. - Highlighted evidence (e.g., malicious payload hashes, suspicious IP reputation). - Recommended remediation steps.

1. Action Execution – One‑click actions feed back into Cloudflare’s Zero Trust and WAF policies, automating containment where appropriate.

The result is a dramatic reduction in mean time to investigate (MTTI) and mean time to respond (MTTR), freeing analysts to focus on strategic threat hunting rather than repetitive triage.

Looking Ahead: Continuous Learning and Expansion

The harness is designed to evolve:

• Feedback Loop – Analyst decisions (e.g., marking an alert as false positive) are fed back into Clef and the LLMs, refining future scoring.
• Extensible Plugins – Organizations can add custom data sources or third‑party threat feeds, which the agents will automatically incorporate.
• Cross‑Cloud Compatibility – While built on Cloudflare’s edge, the architecture can ingest telemetry from other cloud providers, enabling a unified security posture.

Bottom Line

Cloudflare’s evidence‑grounded, agentic security operations harness transforms alert overload into a streamlined, data‑driven process. By automating evidence collection, correlation, and recommendation generation, it cuts analyst fatigue, accelerates response, and strengthens overall security posture.