Dynamic Detection Updates
Ongoing updates to filters, scan logic, detection content, statistical models, indicators, and YARA-X content, as capabilities become available in the product.
Research Intelligence is Cybersnap's continuous research and update engine. Dynamic filters, scan logic, detection content, statistical models, and YARA-X indicators, tuned to your environment and delivered under your control.
Ransomware techniques, extensions, and behaviors change constantly. Detection logic that only updates with a full product release falls behind the threat between releases, right when it matters.
Research Intelligence pushes filter, scan-logic, and detection-content updates on an ongoing basis, matched to your environment, with a clear review and approval path before anything is applied.
Research Intelligence is shown to the customer as a layer in the console: what is new, what is relevant to their environment, why the update matters, and what requires approval.
Ongoing updates to filters, scan logic, detection content, statistical models, indicators, and YARA-X content, as capabilities become available in the product.
Every update is matched to your environment: what changed, which environments may be affected, and why the update is relevant, so the console shows signal, not a generic changelog.
Updates that require customer sign-off go through a review mechanism, approve, or defer, in line with your policy and deployment architecture. Nothing is applied silently.
New detection content does not reach production untested. Every update moves through the same disciplined path before it is available to approve.
Threat research and detection-content development, tracking ransomware families, persistence methods, and indicators as they evolve.
Candidate detection content is tested against known-good and known-bad samples before it is considered for release.
Validated updates are packaged and made available to customers, matched to their environment and context.
Where policy requires it, the customer reviews, approves, or defers, before the update is applied.
Results and false positives observed in the field feed back into ongoing research, closing the loop.
Book a demo and we will walk through how detection content updates, and how approvals work in your environment.