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Weapon Analysis is the capability that turns raw detections into decisions an analyst can act on in seconds. Detection alone is not enough: a security team drowning in unranked alerts is functionally no better off than one with no visibility at all. This capability sits between the correlation engines used by Agentic SOC and the human or AI-agent decision-maker, converting every finding into a ranked, evidence-backed, remediation-ready case file.
The same engine also powers investigation workflows for AgentGuard, where an AI agent's own decision trail needs to be reconstructed and assessed for risk, and for Anforcer, where a suspected fraud pattern in a supply chain needs to be built into a defensible case before it reaches a compliance team. In both contexts the goal is the same: compress the distance between "something looks wrong" and "here is exactly what happened, who did it, and what to do next."
Every candidate finding passes through three stages: classification, where the finding is mapped to a known attack technique or fraud typology; corroboration, where supporting evidence is pulled from adjacent systems and attached to the case; and prioritization, where a risk score is computed from blast radius, asset criticality, and attacker sophistication.
The scoring models are trained and validated against labeled incident data, with full methodology published on our methodology page. Analysts see not just a score but the reasoning chain behind it, which is what makes the tool defensible in front of auditors and executives rather than a black box.
A finding enters the queue already grouped with related events, pre-annotated with MITRE ATT&CK mapping where applicable, and accompanied by a suggested containment action. The analyst reviews, approves, or escalates in a single interface rather than pivoting across five separate tools to reconstruct context manually.
For teams running the Agentic SOC, low-risk, high-confidence cases can be routed to autonomous remediation agents directly, while ambiguous or high-impact cases are always escalated to a human analyst for final sign-off.
Average return on investment for cyber analysts
Less time spent on manual analysis tasks
Precision in threat detection and classification
AI-powered categorization and prioritization of security threats
Continuous evaluation and scoring of security risks
Advanced filtering to reduce noise and focus on real threats
Automated compliance reporting and risk assessment across regulatory frameworks including SOX, GDPR, HIPAA, and industry-specific requirements.
Executive-level dashboards and risk metrics that translate technical security findings into business impact and strategic recommendations.
A trading desk analyst's anomalous data access is classified, corroborated against HR and badge-access records, and escalated with a full evidence package in under five minutes, down from a multi-day manual investigation.
An autonomous procurement agent monitored by AgentGuard attempts an out-of-policy vendor payment; Weapon Analysis builds the decision-trail case file automatically and routes it for compliance review.
Anforcer flags an invoicing anomaly across three regional subsidiaries; the analysis engine correlates shipment, customs, and payment records into a single case ready for legal review.
No. It feeds better-scoped, better-evidenced cases into your existing SOAR or ticketing workflow, and can trigger automated playbooks directly when confidence is high enough.
Findings are ranked by blast radius and confidence rather than raw volume, and related events are grouped into a single case, so analysts review meaningfully fewer, more actionable items rather than a longer undifferentiated queue.
It underpins case-building across Agentic SOC, AgentGuard, and Anforcer Antifraud. For a broader view of how it fits into the full stack, see how the platform works.
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Analyst leverage is the scarcest resource in a modern SOC, and agentic AI attacks consume it faster than any prior threat class. An agent-driven intrusion produces thousands of individually plausible actions instead of a handful of suspicious ones. This capability is built to collapse that volume into a small number of reconstructed narratives an analyst can act on.
Traditional triage assumes a human actor behind each session. Agentic activity breaks that assumption: one compromised AI Principal can drive hundreds of concurrent agent sessions, each individually within normal parameters. The workflow below is what replaces per-alert triage.
Related agent actions are clustered by Principal, intent and target before they reach a human, so the analyst opens one case rather than four hundred alerts.
The case is rendered as an ordered narrative: which Principal issued the instruction, which agents acted, what capability each used, and where intent diverged from approved scope.
Every system, dataset and downstream agent reachable from the implicated Principal is enumerated, converting the open question 'what else did it touch?' into a finite list.
The case ships with containment actions, the policy that should have blocked the behavior, and a decision record suitable for audit and post-incident review.
Different agentic threat classes require different reconstruction. The capability is tuned for four that conventional tooling handles poorly.
An agent chains legitimate capabilities across systems — read a ticket, fetch a credential, call an internal API. Each step passes access control; the sequence is the attack. Analysis scores the sequence, not the step.
When the system that creates and instructs agents is subverted, every agent it governs inherits the compromise. Analysis treats Principal-level anomalies as population events, not single-identity events.
A trusted SaaS vendor ships an agentic feature that reaches your data without a procurement decision. Analysis surfaces the new non-human actor, its scope, and the exposure it introduced behind your back.
Autonomous crawlers and probing agents map your exposed surface at machine speed. Analysis distinguishes them from ordinary traffic and links reconnaissance patterns to later intrusion attempts.
The operational shift is from alert volume to case quality. Analysts stop hand-correlating model logs, gateway logs and cloud trails and start reviewing reconstructed narratives with attribution already resolved.
Tier-one work moves from classification to verification. Tier-two work moves from evidence assembly to decision-making. Investigation cycle time — the honest measure of analyst leverage — is the metric to baseline before deployment and report after.
Reporting also changes. Because every case carries a decision record, post-incident review and regulatory reporting are produced from the same artifact the analyst already used, rather than reconstructed weeks later from partial logs.
Analysis consumes attributed telemetry produced by the platform's processing layer and enriches cases with policy context from AgentGuard, so an analyst sees not only what happened but which policy applied and whether it was enforced.
Findings feed Agentic SOC for autonomous containment, and external reconnaissance signals correlate with AgentHunter's view of attacker-side agent activity, closing the loop between what is probing you and what is happening inside.
No. It sits above them. SIEM and EDR remain the record for human and endpoint activity; this capability adds the agent and AI Principal layer they were never designed to attribute.
Clustering happens before human review, so noise is reduced structurally rather than by suppression rules. Analysts see cases, and suppression decisions apply to behaviors rather than to individual alerts.
Each case carries an immutable decision record with identity, intent, policy applied and outcome, which is the evidence form auditors and regulators increasingly ask for regarding autonomous systems.
Coverage is at the action and identity layer rather than the model layer, so commercial model APIs, self-hosted models and third-party agent frameworks are analyzed through the same schema.
No. Cases are presented as narratives in security language. AI-specific context — Principal, model, capability scope — is supplied by the platform rather than assumed knowledge.
Because ingestion uses existing gateways, identity providers and cloud audit trails, most environments produce attributed cases without deploying anything on endpoints.