Tenable (with AI)
Integrations
- ServiceNow
- Terraform
- AWS IAM
- Azure Active Directory
- Palo Alto XSOAR
- Ansible
Pricing Details
- Licensing is calculated per asset (IP, Cloud Resource, or Identity).
- ExposureAI features, including Generative Remediation Guidance, are typically bundled within Tenable One Enterprise tiers.
Features
- Exposure Graph with CIEM Integration
- Generative Remediation & Playbook Generation
- Private AI Proxy for Data Masking
- Toxic Combination Analysis
- Agentless Cloud Asset Discovery
- Natural Language Exposure Search
Description
Tenable ExposureAI: Exposure Graph & Generative Remediation Review
As of 2026, the Tenable One platform has evolved into a comprehensive exposure management ecosystem centered on the Exposure Graph. This engine moves beyond traditional vulnerability scanning by performing agentless analysis of cloud permissions and entitlements (CIEM), effectively mapping the relationship between identities and potential exploit vectors 📑. The platform's processing pipeline is secured by a Private AI Proxy, which intercepts and redacts sensitive metadata before telemetry interacts with Large Language Models 📑.
Exposure Graph: Contextual Asset & Identity Fusion
The core innovation lies in the integration of Ermetic-derived technologies, enabling the platform to correlate software vulnerabilities with excessive cloud permissions 📑.
- Identity & Entitlement Correlation (CIEM): Automatically identifies risk escalation paths where misconfigured identities provide lateral movement opportunities across multi-cloud environments 📑.
- Toxic Combinations: A specialized analysis that flags the intersection of unpatched vulnerabilities, reachable workloads, and high-privilege credentials 📑.
- Risk Calibration: Uses a Managed Persistence Layer to store cross-context telemetry, though the specific high-concurrency database implementation remains undisclosed 🌑.
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ExposureAI Search & Generative Remediation Guidance
The generative AI component acts as a natural language orchestration layer, transforming raw exposure data into actionable engineering tasks 📑.
- Generative Remediation Playbooks: The system generates ready-to-deploy configurations (e.g., Terraform scripts, Ansible playbooks) to mitigate identified risks 📑.
- Privacy-Preserving AI Gateway: Employs an architectural buffer that ensures PII and proprietary network schemas are masked during the inferencing process 📑.
- Model Agnosticism: While the interface is unified, the specific underlying foundational models (LLMs) used for remediation logic are not publicly specified 🌑.
Evaluation Guidance
Technical architects should focus on the validation of generative playbooks within isolated sandbox environments to ensure infrastructure-as-code (IaC) outputs align with internal compliance standards 📑. Organizations must audit the Private AI Proxy configurations to verify that all custom sensitive data types are correctly identified for masking 🧠. Evaluate the integration latency between CIEM telemetry and the Exposure Graph to ensure real-time visibility during rapid cloud scaling 🌑.
Release History
End-of-year update featuring autonomous risk mitigation workflows. AI-driven prioritization of identity-based risks across multi-cloud environments.
Integration of the Exposure Graph. A high-scale data engine that uses AI to visualize toxic combinations of vulnerabilities and misconfigurations.
Full release of the Generative AI search assistant. Security teams can ask complex questions about their exposure in natural language.
Major update to the AI-driven Attack Path Analysis. Real-time mapping of relationships between assets to identify the 'critical path' to sensitive data.
Official launch of ExposureAI. Integration of generative AI to summarize attack paths and provide natural language search capabilities.
Launch of the unified Exposure Management platform. Shift from point-in-time scanning to continuous visibility across cloud, OT, and identity.
Introduction of VPR (Vulnerability Priority Rating). First major use of machine learning to predict which vulnerabilities are likely to be exploited.
Tool Pros and Cons
Pros
- AI-driven risk prioritization
- Comprehensive exposure analysis
- Proactive threat detection
- Reduced alert fatigue
- Automated remediation
Cons
- Potentially expensive
- Steep learning curve
- Data quality dependent
