Today, Cybersnap AI supports recovery teams with AI-assisted Recovery Intelligence. The direction is Agentic Recovery Assurance: coordinated agent roles that predict, contain, validate, decide, and prove across the recovery process. The long-term direction is policy-governed autonomous recovery, with guardrails and human approval where required. The agent architecture below is roadmap direction, not a current product claim.
A single AI cyber agent over SnapMap data. Summarizes risk, validates threats, maps affected components, detects anomalies, explains indicators, and suggests next actions. Built. Deployed. Operating in real customer environments.
Coordinated agent roles — Predict & Detect, React & Contain, Rescue & Validate, Recovery Decision, and Prevent & Prove — each focused on one part of the recovery problem and feeding a central Recovery Decision. In routine it continuously builds readiness; during an attack the same layer supports the decision. The long-term direction is policy-governed autonomous recovery with guardrails. Roadmap direction; not all capabilities are generally available today.
Cyber recovery is not one question. It is many at once: what happened, when did it likely begin, which workloads were touched, which recovery points carry increased risk, which candidate presents the strongest evidence, what needs isolation, what can resume now. Agentic Recovery Assurance is designed to work them in parallel instead of forcing humans to debate them in a war room. This is roadmap direction.
Traditional recovery software adds capability one feature at a time. More features require more teams, more tickets, more QA cycles, more releases. That model is reliable but slow. And when ransomware hits, the recovery decision still falls back to humans debating restore points under time pressure.
Each agent role focuses on one job, improves at that job, and feeds findings into a central Recovery Decision. A detection role does not behave like a decision role. A containment role does not behave like a validation role. The platform is designed to stop scaling linearly and start compounding across customers, environments, and threat patterns.
Each agent role does one job, improves at that job, and feeds findings into the Recovery Decision. Specialization is the value. This architecture is the Agentic Recovery Assurance direction; not all roles are generally available today.
Finds early cyber signals across recovery evidence and production history, builds the attack timeline, surfaces mass-change patterns and file-behavior anomalies, and highlights when suspicious activity likely began.
Guides or triggers policy-controlled containment actions, informed by the environment's infrastructure, defenses, and recovery policies, so response stays specific to this environment rather than generic.
Evaluates recovery candidates and coordinates recovery validation in an isolated environment. Tests usability, integrity, and reinfection risk before any candidate touches production.
Pulls findings from every agent role, ranks recovery candidates by confidence, explains the reasoning, and recommends the recovery path the team can act on. This is the role that compounds the work of all the others into one Recovery Decision.
Identifies recovery exposure and continuously improves recovery readiness before the real incident: which workloads to watch, which need investigation, which require isolation, and what the team should do next, so the organization can prove it can recover.
Linear software adds capability one feature at a time. Agentic Recovery Assurance compounds capability across every customer, environment, storage platform, threat pattern, and recovery workflow. Each new agent specializes, improves, and feeds the brain. The engine gets sharper as the platform expands.
A forensic agent does not behave like a ranking agent. Each agent improves at one job rather than diluting across many.
The questions in a recovery decision get answered at the same time, not in a sequential war-room handoff.
Every new agent makes the platform sharper across every existing customer and environment. The curve bends.
policy-governed autonomous recovery is the long-term direction: inspect snapshot history, prioritize recovery candidates, isolate questionable recovery points, validate workloads, and guide guided production resume. Policy-governed. Evidence-based. Human-approved where required.
Cleared for restore with full audit trail. The most recent point where multi-signal validation agrees.
Cannot auto-clear. Surfaces the specific findings driving uncertainty and recommends investigation order.
Restoring this point would likely reintroduce the attacker. Move backward in time to find the next clean candidate.
Autonomous recovery must be policy-governed, evidence-based, validated, and human-approved where required. The product, the company, and the roadmap converge on guided production resume in minutes.
A single AI cyber agent analyzes production evidence, timelines, scan results, recovery candidates, anomaly priorities, user activity, and validation outputs.
Specialized agents coordinated by an Agentic Recovery Assurance orchestrator. Investigation, validation, and recovery decisioning compressed into a single policy-governed workflow built for ransomware pressure.
From guided recovery decisions toward policy-governed autonomous recovery: retrospective attack discovery, recovery exposure simulation, clean-room validation, policy-governed rescue actions, and guided production resume in minutes.
Ransomware already operates at machine speed. Recovery still depends on humans debating restore points under pressure. The next control layer is the Agentic Recovery Assurance orchestrator, coordinating specialized agents across production evidence and deciding what can safely resume, before downtime becomes business damage.
Cybersnap.io is building that layer, one validated capability at a time.
Book a strategic briefing. We will walk you through what Cybersnap AI does today, the multi-agent direction, and the path to policy-governed autonomous recovery.