IONIX Attack Surface Management Platform

Continuously enumerates internet-exposed assets (domains, IPs, subdomains, certificates, cloud storage, APIs) using passive DNS, certificate transparency logs, and active probing, including assets outside the official inventory.

Enumerates and monitors the attack surface of subsidiaries, acquired companies, and affiliated brands, common gap during M&A activity when new infrastructure is inherited without full visibility.

Identifies cloud resources, SaaS applications, and exposed services deployed by business units without IT or security team visibility or approval.

Identifies software stacks, versions, and components running on discovered assets through passive banner analysis and active probing, mapping CVE exposure without authenticated scanning.

Tracks SSL/TLS certificate expirations, newly registered lookalike domains, and subdomain takeover opportunities (dangling DNS records pointing to deprovisioned cloud services).

Ranks discovered exposures by combining exploitability signals, asset business context, and active threat intelligence to produce an actionable remediation queue.

Discovers assets and their exposures across the external, internal, cloud, and end-user attack surfaces, covering endpoints, network and on-premises infrastructure, identities and entitlements, hosts, containers, IoT and OT, and cloud platforms and applications, either through native discovery or by integrating third-party discovery sources, and reports vulnerabilities, misconfigurations, unmanaged assets, and compliance gaps in one inventory.

Confirms whether prioritized exposures are actually exploitable by running or ingesting adversarial validation results, such as breach and attack simulation or automated penetration testing delivered natively or by an integrated third-party tool, and re-ranks or closes exposures on the outcome so the queue reflects confirmed rather than theoretical risk.

Ranks exposures by their accessibility, visibility, and exploitability combined with asset criticality, business impact, and the security controls already in place, so a medium-severity issue on a critical, reachable, unprotected service outranks a high-severity issue on an isolated or compensated one.

Creates and tracks remediation tasks across teams and ticketing systems, measuring exposure reduction over time rather than simply listing open findings.

Models how exposures chain across assets and identities to reach critical systems, mapping attack paths and blast radius to separate reachable crown-jewel risks from dead ends.

Maps the discovered exposure inventory against active threat actor targeting and in-the-wild exploitation data to surface vulnerabilities under active attack.

Tracks the life cycle of exposures through a centralized, aggregated view supported by automated workflows.

Pushes a mitigation for a prioritized exposure directly to a security control, for example a firewall, endpoint, or posture-management rule, as a compensating measure when a patch is unavailable or delayed, and tracks that mitigation alongside the exposure until it is remediated.

Groups assets into business processes, applications, or protection surfaces with named owners and criticality, so each exposure management cycle is scoped to what the business must protect and exposure is assessed and reported per scope rather than across the whole estate.

Extends exposure discovery to digital assets and artifacts that external threat actors are actively abusing, such as leaked credentials, lookalike domains, exposed code, or digital supply-chain components seen on social media and the surface, deep, and dark web, natively or through a third-party feed, and folds them into the same exposure inventory and prioritization as internal findings.

Uses generative AI to produce exposure-specific fix instructions, scripts, or remediation playbooks from the finding and its asset context, so remediation owners receive an actionable plan instead of a generic advisory.