Today, Cybersnap AI identifies the closest clean snapshot before the attack. Next is an AI-native recovery engine: specialized agents that each focus on one part of the recovery problem and compound capability across customers, environments, and threat patterns. The endpoint is AI AutoRescue, policy-governed and human-approved where required.
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.
An AI-native recovery engine. Specialized agents for forensics, customer-profile mapping, simulation, ranking, and recommendation, each focused on one job and feeding a central recovery brain. The platform stops adding features and starts compounding capability. Policy-governed AI AutoRescue is the endpoint.
Cyber recovery is not one question. It is many at once: what happened, when did it begin, which workloads were touched, which snapshots are contaminated, which point is closest to production and still clean, what needs isolation, what can safely resume now. Multi-Agent Recovery Intelligence answers them in parallel instead of forcing humans to debate them in a war room.
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 focuses on one job, improves at that job, and feeds findings into a central recovery brain. A forensic agent does not behave like a recovery-ranking agent. A customer-profile agent does not behave like a simulation agent. The platform stops scaling linearly and starts compounding across customers, environments, and threat patterns.
Each agent does one job, improves at that job, and feeds findings into the recovery brain. Specialization is the value. The architecture compounds capability across customers, environments, and threat patterns rather than scaling one workflow.
Analyzes ransomware indicators across snapshot history, builds the attack timeline, surfaces mass-change patterns and file-behavior anomalies, and identifies when suspicious activity actually began.
Maps the customer's infrastructure profile: storage, workloads, defenses, recovery policies, and weak points. Gives every other agent the context to make recovery decisions specific to this environment, not generic.
Runs controlled simulations on candidate recovery points in an isolated environment. Tests usability, integrity, and re-infection risk before any candidate touches production.
Pulls findings from every other agent, ranks clean recovery candidates by confidence, and produces the verdict the team can act on. This is the agent that compounds the work of all the others into one decision.
Composes the final operational recommendation: which workloads resume now, which need investigation, which require isolation, and what the team should do next. Tied to the customer profile and the policy in force.
Linear software adds capability one feature at a time. Multi-Agent Recovery Intelligence 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.
AI AutoRescue is the long-term direction: inspect snapshot history, identify clean candidates, isolate questionable recovery points, validate workloads, and guide safe 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 safe 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 AI Cyber Orchestrator. Investigation, validation, and recovery decisioning compressed into a single policy-governed workflow built for ransomware pressure.
From guided recovery decisions toward AI AutoRescue: retrospective attack discovery, recovery exposure simulation, clean-room validation, policy-governed rescue actions, and safe 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 AI Cyber 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 AI AutoRescue.