AttackIQ CTEM Platform

Provides specific detection rule recommendations, log source requirements, and control configuration changes for each identified gap: not just a list of undetected techniques.

A scenario authoring workbench where advanced users build and chain custom validation tests, defining attack actions, success criteria, and cleanup steps. Lets red and blue teams create exercises beyond the vendor's prebuilt library.

Executes simulations using non-destructive payloads and read-only techniques that cannot cause data loss, service disruption, or lateral damage in production environments.

Runs attack technique sequences on a scheduled or continuous basis against production controls, surfacing control drift between point-in-time assessments without human intervention.

Maps executed attack techniques to the MITRE ATT&CK framework and reports coverage across the attack lifecycle, enabling threat-informed gap analysis and detection engineering.

Executes cloud-specific attack techniques including IAM privilege escalation, SSRF to metadata services, storage bucket enumeration, and cross-account role assumption to surface cloud exploit paths.

Reports which executed techniques triggered alerts in existing security controls and which did not, mapping undetected techniques to the specific control or detection rule that should have fired.

Provides a continuously updated, vendor-supplied library of pre-built attack scenarios and techniques spanning the full kill chain, runnable at scale with little to no offensive expertise required.

Pulls current threat intelligence from native feeds or third-party integrations to build and run validations against newly disclosed threats, letting teams confirm whether defenses block an emerging campaign or CVE shortly after it is published.

Trends control efficacy and validated exposure across runs and baselines results against industry peers, giving executives and asset owners scorecards that show whether security posture is improving rather than a one-time list of findings.

Uses LLMs or AI agents in the validation control plane to choose and prioritize attack scenarios from a natural-language request, interpret validation results, and draft the mobilization steps, so teams without offensive-security skills can run and act on validations.

Ranks remediation by the impact of validated attack paths and blast radius rather than raw CVSS scores, directing effort toward the weaknesses that actually enable compromise.

Re-tests specific validated weaknesses after remediation to confirm each fix closed the attack path, closing the validation loop between testing and remediation.

Dynamically discovers and chains exposures (unpatched CVEs, misconfigurations, and credential weaknesses) into multi-step exploit paths without predefined scripts, sequencing weaknesses in the order an attacker would based on live environment state.

Ingests estate context such as asset discovery, attack surface management, and vulnerability data, natively or through integrations, to scope and prioritize validation against the assets and exposures that matter most.

Replicates the tactics, techniques and procedures of specific named threat actors as multi-step campaigns, distinct from breadth-first technique simulation, so that defenses are tested against the adversaries most likely to target the organization's sector. Depth of actor libraries and the speed at which new campaigns and zero-day techniques are added vary across vendors.

Generates trend reports on exposure posture (new exposure, remediated exposure, outstanding exposure by severity), in business language suitable for security program reviews.

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

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

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.

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.

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.

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

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.

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.

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.