Wiz Cloud and AI Security Platform

Instruments workload behavior at the kernel level via eBPF without a traditional user-space agent. Provides syscall-level visibility into process execution, network connections, and file access in running containers and VMs.

Enriches cloud misconfigurations, vulnerable workloads, and runtime detections with threat intelligence on active exploitation, prioritizing exposures attackers use over theoretical severity alone.

Correlates individual misconfigurations, CVEs and excessive entitlements into chained attack scenarios showing lateral movement paths from an exposed entry point to a target asset, visualized on the resource graph. Produces a prioritized list of attack paths rather than a flat CVE inventory. Products differ in whether they show only possible paths derived from posture data or also actual paths confirmed from runtime and log telemetry.

Analyzes container images and dependencies for CVEs, malicious or compromised packages, and SBOM generation across the build pipeline.

Maps the effective access of human and machine identities to compute, storage and data resources across AWS, Azure and GCP as an access relationship graph, surfacing over-permissioned roles, unused permissions, cross-account trust and toxic combinations of administrator permissions, and remediating them toward least privilege, including automatic revocation of excessive roles.

Enforces a single policy definition across AWS, Azure, and GCP resource types, translating to provider-native configurations rather than requiring separate policy sets per cloud.

Monitors running pod and container behavior against policy, detecting unexpected process execution, network connections, and privilege escalation at runtime rather than at image scan time.

Delivers scan results inside developer IDEs and pipeline stages so developers receive findings before code merges, reducing the cost and cycle time of remediation.

Exports compliance evidence pre-mapped to framework control requirements (SOC 2, ISO 27001, PCI DSS), in formats auditors can consume directly: not raw CSV exports requiring manual assembly.

Reads cloud volume snapshots out-of-band to assess workloads for vulnerabilities, malware, exposed secrets and misconfigurations without installing agents or touching running instances, on a configurable scan schedule. Coverage of Windows threat detection and file integrity checks in agentless mode varies across products.

Discovers and classifies sensitive data in IaaS and PaaS stores such as object storage, databases, and data warehouses, surfacing data exposure risk alongside infrastructure findings.

Scans infrastructure-as-code templates (Terraform, CloudFormation, Kubernetes manifests and Helm charts) for misconfigurations, policy violations and embedded secrets before deployment, gates CI/CD pipelines on the resulting risk, and detects drift between the IaC definition and the deployed resource. Depth of productized pipeline integration and drift remediation varies across products.

Continuously audits cloud and Kubernetes configuration across AWS, Azure, and GCP against security benchmarks, flagging misconfigurations and identity-permission gaps that create exploitable exposures.

Supports on-premises or air-gapped artifact and workload inspection under customer control, so regulated or sovereign data never leaves the customer boundary.

Controls who can administer the platform through role-based access with custom and inherited administrator roles, an organizational hierarchy with sub-tenants or workspaces for business units and managed customers, and out-of-the-box identity federation (OIDC and SAML) for administrator sign-in. Depth of multitenancy and custom role definition varies across products.

Pushes findings into help-desk ticketing, SIEM and security analytics, SOAR, asset management and application security tools and pulls status back, so remediation ownership, closure and exceptions stay synchronized between the platform and the SOC or developer workflow instead of being re-keyed. Productized, bidirectional depth of these integrations varies across products.

Assesses the configuration of Kubernetes clusters and managed orchestrators (EKS, AKS, GKE, ECS, Fargate, OpenShift) against best-practice templates, surfacing cluster misconfigurations, weak RBAC, exposed control planes and configuration drift, and driving their remediation. Distinct from runtime workload monitoring: this is the posture of the orchestrator itself.

Provides AI copilots or agents inside the platform that search product documentation, triage and investigate alerts with plain-language explanations, discover threats and indicators of attack from telemetry, and generate remediation steps, policies and playbooks. Products differ in which of these tasks the copilot performs and how much of the investigation it completes on its own.

Detects sensitive or regulated data in AI training, fine-tuning, or third-party LLM flows without appropriate controls, such as unencrypted PII in inputs or PHI sent to external APIs.

Maps data lineage and provenance across AI training and inference pipelines, tracing how PII, PHI, and IP move into models and external services.

Assesses the identities and service accounts that AI models, pipelines, and agents use, flagging over-permissioned non-human identities and access paths that violate least privilege. Reports identity risk as a posture finding, distinct from enforcing access policies at the model API at runtime.

Automatically discovers AI models, LLM API connections, ML pipelines, and AI-enabled SaaS applications in use across the organization, including those deployed without IT authorization.

Discovers AI model and inference endpoints and flags public exposure, weak authentication, default credentials, or excessive permissions as posture misconfigurations.

Monitors AI-agent behavior at runtime to detect anomalous or malicious actions and policy violations.

Verifies that the organization's sensitive data is stored and processed only in approved geographic regions, mapping discovered data locations and cross-region transfers to applicable residency requirements (GDPR and the EEA, Australian Privacy Act, sectoral data localization laws) and to rules on where AI services may process it. Distinct from Data Sovereignty Controls, which governs where the scanning product itself handles content.

Assigns risk scores to discovered data based on sensitivity, access exposure, and configuration, then continuously monitors access patterns and policy compliance to surface the highest-risk data stores for action.

Maps effective permissions to sensitive data stores across cloud IAM, database roles, and SaaS permissions, identifies over-privileged access and dormant entitlements.

Discovers and classifies sensitive data (PII, PHI, payment data, IP, secrets) across structured and unstructured stores by combining deterministic techniques such as patterns, keywords, and validators with AI/ML techniques such as unsupervised clustering and small language models. Breadth of the technique blend, and whether classification extends to prompts, model outputs, and vector databases, are the primary differentiators; products that rely on pattern matching alone sit at the low end.

Maps how sensitive data moves and transforms through AI pipelines, including model training sets, third-party AI API calls, prompts and model outputs, and vector databases holding embeddings, and flags where regulated data is exposed to a model or a downstream AI service. Depth of coverage for embeddings, fine-tuning data, and third-party AI platforms varies across products.

Identifies sensitive data in locations outside authorized data stores, development databases containing production PII, unprotected S3 prefixes, forgotten data lake partitions.

Baselines how users and service accounts normally access sensitive data stores and flags unusual access behavior in real time, such as mass downloads, off-hours access, or first-time access to regulated data, with detailed audit logs for investigating insider risk and compromised accounts. Often sold as data detection and response (DDR); products differ in whether detection uses ML baselining or static rules.

Extends access analysis to non-human AI identities, mapping which AI agents, copilots, and stand-alone models can reach which sensitive data stores and flagging over-broad or unsanctioned model access before it is exploited. Coverage of agent frameworks and model identities, and whether findings feed entitlement right-sizing before an AI rollout, vary across products.

Enriches classification results with context beyond the content itself, such as data lineage, effective permissions, storage location, owner, and business metadata, so that a record is labeled by what it is and how it is used rather than by pattern matches alone. Depth of contextual inputs, and whether they change the assigned sensitivity, vary widely across products.

Improves classification precision over time through administrator false-positive flagging, classifier threshold and rule tuning, custom classifier authoring, and workflows that route uncertain results to data owners for validation or exception handling. Whether stakeholder feedback retrains the classifiers, or only suppresses individual findings, is the primary differentiator.

Discovers and classifies sensitive data across a heterogeneous cloud estate in one inventory: object storage, managed data warehouses and lakes, cloud database services, and SaaS applications, including sources that are not supported out of the box through custom connectors. Breadth of supported sources and depth per source vary; on-premises and mainframe estates are covered under On-Premises and Mainframe Data Discovery.

Produces audit trails and regulation-mapped reports such as GDPR, HIPAA, and PCI DSS data inventories from discovery and access findings, with alerts on policy violations, so that evidence of data-handling practices can be handed to auditors without manual assembly. Custom and stakeholder-specific reporting is a common weak spot across products.