Security | Threat Detection | Cyberattacks | DevSecOps | Compliance

The Role of AI Calling in Secure Business Communication

The Role of AI Calling in Secure Business Communication

Oct 14, 2026 By SecuritySenses In SecuritySenses
Every business depends on phone conversations. Customers share account details, teams discuss deals, and managers make quick decisions over the line. Because so much sensitive information travels through voice, protecting it matters as much as protecting email or stored files. This is where AI calling has started to play a useful part. In simple terms, AI calling means using artificial intelligence to place, answer, or manage phone calls, often by understanding speech and responding in a natural way. When it is set up with care, it can make business communication safer as well as smoother.
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netwrix

What Are Unmanaged Data Stores and How to Secure Them

Oct 7, 2026 By Netwrix Team In Netwrix
Unmanaged data stores let permissions drift while sensitive data accumulates unnoticed. They are repositories an organization doesn’t actively govern, leaving them without an owner, an access-review cadence, or a retention policy. Securing them means assigning ownership, correcting access, and maintaining continuous governance and visibility.
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Falcon Data Security for SaaS - Secure Sensitive Data in Microsoft 365

Oct 7, 2026 By CrowdStrike In CrowdStrike
See CrowdStrike Falcon Data Security for SaaS in action and learn how to discover, classify, and protect sensitive data across Microsoft 365. See how security teams can identify sensitive data in SharePoint and OneDrive, prioritize exposure, automatically apply Microsoft Sensitivity Labels, reduce unintended Microsoft Copilot exposure, and extend protection with just-in-time access.
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Difference between Network DLP vs Endpoint DLP vs Cloud DLP

Oct 6, 2026 By Prashant Bhivsane In miniOrange
When it comes to protecting business-sensitive data, understanding the difference and the scope of Network DLP, Endpoint DLP, and Cloud DLP is essential. Each of these Data Loss Prevention solutions (DLP) plays a unique role in securing data across various environments, whether it is on the Network, on individual devices, or in the Cloud. Knowing how each solution works can help you determine the best approach to safeguard your organization's sensitive information.
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nightfall

Meta Muse in the Enterprise: Data Security for Personal Autonomous Agents

Oct 5, 2026 By The Nightfall Team In Nightfall
The deployment of personal AI agents like Meta's Muse represents a structural shift in enterprise data loss prevention. Unlike conversational web chatbots where data movement is limited to manual user prompts, autonomous personal agents operate in cloud virtual machines, run continuous background tasks, and connect directly to enterprise SaaS platforms through OAuth integrations.
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nightfall

September 2026 Product Updates

Oct 5, 2026 By The Nightfall Team In Nightfall
September was the month Nightfall's control layer reached inside the model call. Five releases shipped: Claude Inference Hooks, MCP Gateway, expanded endpoint exfiltration controls, personal file classifiers, and PII coverage across 30 countries. Together they close the gap between what security teams can control when a person moves data and what they can control when an AI agent does.
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CrowdStrike

Falcon Data Security for SaaS Secures Sensitive Data in Microsoft 365

Oct 5, 2026 By Dana Larson In CrowdStrike
For many organizations, Microsoft 365 environments are essential to data security strategy. Their strategic plans live in SharePoint; their employee records and customer data are stored in OneDrive. Teams use these tools to collaborate on projects every day, and increasingly, AI tools like Microsoft Copilot can access the information they rely on.
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cyberhaven

How to Build a Data Security Program for Generative AI

Oct 1, 2026 By Cyberhaven In Cyberhaven
Ask five security leaders what "data security for generative AI" covers and you'll get five different answers. Some may point to an existing DLP rule for ChatGPT, while others would point to an AI acceptable use policy. Some operate under the assumption that their DSPM vendor already handles genAI security. The confusion isn't about effort, as most teams are actively trying to get ahead of GenAI risk.
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