> For the complete documentation index, see [llms.txt](https://cortex-docs.paloaltonetworks.com/llms.txt). Markdown versions of documentation pages are available by appending `.md` to page URLs; this page is available as [Markdown](https://cortex-docs.paloaltonetworks.com/cortex-data-security/release-information/features-2026/july-2026/release-highlights.md).

# Release Highlights

The following are the key highlights for this release.

| FEATURE                                            | DESCRIPTION                                                                                                                                                                                                                                                             |
| -------------------------------------------------- | ----------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------- |
| Unified Policy Management                          | Harmonizes data security policies across the enterprise. This provides a consistent classification schema across all supported data stores and eliminates policy drift between disparate environments.                                                                  |
| Data Security Posture Management (DSPM)            | Automatically discovers and catalogs sensitive data at rest across Cloud, SaaS, DBaaS, and on-premises environments. It utilizes an engine with hundreds of out-of-the-box classifiers, LLM-powered classification, OCR for images and PDFs, and MIP label integration. |
| AI Security Posture Management (AI-SPM)            | Provides end-to-end visibility into AI pipelines by mapping models, agents, and training or inference datasets. It prioritizes risks measured against standards such as NIST 600-1 and the OWASP Top 10 for LLMs.                                                       |
| Data Access Governance (DAG) and Identity Security | Analyzes identities and entitlements to calculate effective permissions and help you rightsize access to sensitive data. It enables you to enforce least privilege across humans, service accounts, and AI agents.                                                      |
| Data Detection and Response (DDR)                  | Uses machine learning and behavioral models to monitor activity for real-time threats. The platform identifies data-centric anomalies such as mass exfiltration, ransomware behavior, and risky data sharing while filtering out unrelated security noise.              |
| Automated and Guided Remediation                   | Provides a path to resolution through step-by-step instructions or automated playbooks to remediate exposures. Examples include masking sensitive data, enforcing encryption, or rightsizing over-privileged permissions.                                               |
| Data Security Assistant (Agentic AI)               | A natural language interface that simplifies data operations. Users can ask the assistant to explore data inventory (example: "Show me all sensitive tables in production"), generate XQL queries, and create on-the-fly visualizations directly from chat.             |


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