All news with #data loss prevention tag

🔒 Microsoft is extending Purview data loss prevention controls so the Microsoft 365 Copilot assistant cannot read or process sensitive Word, Excel, and PowerPoint files regardless of where they are stored. The change leverages the Office Augmentation Loop (AugLoop) component so Office clients can supply sensitivity labels for local files as well as for SharePoint and OneDrive. Microsoft will roll out the update between late March and late April 2026 and says it will be automatically enabled for tenants with DLP policies configured to block Copilot. The move follows a January bug that briefly allowed Copilot Chat to access and summarize protected emails.

⚠️Microsoft says a bug in Microsoft 365 Copilot has been summarizing confidential emails since late January, bypassing organizations' configured data loss prevention (DLP) safeguards. The flaw affected the Copilot 'work tab' chat and improperly read messages stored in Sent Items and Drafts, including those with sensitivity labels intended to block automated processing. Microsoft attributes the behavior to a code error, began rolling out a fix in early February, and is monitoring deployment while contacting a subset of impacted users. The company has not yet disclosed the full scope or number of affected organizations and has flagged the incident as an advisory.

🛡️ Organizations face escalating data-exfiltration and malicious-code risks as consumer GenAI tools proliferate. Legacy DLP solutions are costly and complex, while unmanaged GenAI enables staff to upload PII, PHI and proprietary IP to public models. The author outlines two practical paths: enterprise GenAI licenses with built-in controls or deploying XDR/MDR DLP to enforce detection and automated response at endpoints. For many firms, the latter is presented as a cost-effective, risk-aware option that balances innovation and protection.

🔒 Most security leaders assume they know where sensitive data resides, but rapid AI adoption has created a new exposure surface in AI inference traffic. Prompts often contain source code, contracts, PII and proprietary workflows that flow through application layers, logs and third‑party services without classification or adequate controls. Traditional protections — transport encryption, legacy DLP and standard logging practices — frequently fail to prevent prompt leakage, producing an often invisible and growing enterprise risk.

🔐 In a connected business environment, the article argues that conventional perimeter controls like firewalls and intrusion-detection systems are no longer sufficient to protect organisations. It highlights how a $280 billion data-broker industry and billions of daily phishing emails create an expansive, often invisible outbound data flow that enables credible CEO fraud and targeted spear-phishing. The author recommends deploying Security & Privacy Boxes, strengthening employee training, self-hosting sensitive services and adopting a Zero Trust approach to reduce leakage and long-term APT dwell time.

🔒 Web browsers remain a primary enterprise attack surface, and the market for secure browsers is maturing as vendors and hyperscalers fold browser isolation into broader security platforms. The article summarizes evaluation criteria — from MFA, isolation and DLP to extension control, logging and anonymous surfing — and highlights recent consolidation and vendor offerings. It emphasizes integration, support and cost tradeoffs when choosing a deployment mode.

🔒 Microsoft 365 sharing is convenient but can quickly lead to uncontrolled access and data exposure. This sponsored article explains how tenfold provides centralized visibility across Teams, OneDrive and SharePoint and introduces targeted access reviews for shared content. Personalized review dashboards let owners confirm or revoke links, and automated enforcement removes permissions that fail review.

🔒 HoundDog.ai provides a privacy-first static code scanner that embeds detection and governance into development to prevent data leaks before code reaches production. The Rust-based engine performs deep interprocedural analysis across files and functions and can scan millions of lines in under a minute. It traces more than 100 sensitive data types into risky sinks such as logs, LLM prompts, files, local storage, and third-party SDKs, and integrates with IDEs and CI to enforce allowlists and auto-generate RoPA, PIA and DPIA evidence.

🔒 Even well-defended environments can be undermined by 'shadow spreadsheets'—ad hoc Sheets or Excel files users share because official tools don't meet every need. These files proliferate, evade DLP and audit visibility, and create an unmanageable attack surface. Training or heavy-handed lockdowns often fail, while custom apps are costly and slow. The piece recommends Grist, a self-hostable, spreadsheet-like platform built on a relational back end with RBAC and audit logging to restore a single source of truth.

🔒 The article argues that the browser is now the primary interface for enterprise GenAI and outlines a practical security model combining policy, isolation, and precision data controls. It recommends categorizing GenAI services into sanctioned and public tools, enforcing SSO for corporate identities, and preventing cross‑account leakage. The piece highlights the risks of prompt copy/paste, file uploads, and extensions, and advises per‑site/session controls, telemetry, and a pragmatic 30‑day Secure Enterprise Browser (SEB) rollout to enable safe, productive use.

🔐 This article explains how sensitive data commonly leaks from AI systems — from RAG retrievals and agentic tool chains to user-initiated oversharing — and why LLMs cannot enforce document-level permissions. It recommends a layered, defense-in-depth approach: automatic identification and classification, data minimization at ingress, sanitization, redaction, and strict access controls that follow data through the pipeline. The authors also stress threat modeling and vendor due diligence to limit regulatory, competitive, and reputational harm.

🔒 This guide presents a practical defense-in-depth approach to move generative AI projects from prototype to production by protecting the application, data, and infrastructure layers. It includes hands-on labs demonstrating how to deploy Model Armor for real-time prompt and response inspection, implement Sensitive Data Protection pipelines to detect and de-identify PII, and harden compute and storage with private VPCs, Secure Boot, and service perimeter controls. Reusable templates, automated jobs, and integration blueprints help teams reduce prompt injection, data leakage, and exfiltration risk while aligning operational controls with compliance and privacy expectations.

🔒 Amazon Connect now intercepts chat messages before delivery to enable automatic sensitive-data redaction and custom message processing. The built-in redaction detects entities such as credit card and social security numbers across multiple language variants and can replace them with generic or entity-specific placeholders (e.g., [PII] or [NAME]). Businesses can also integrate custom processors for translation, profanity filtering, or other transformations to meet compliance and CX needs. The feature is available in multiple AWS regions.

🔒 AWS has launched Network Firewall Proxy in public preview, providing centralized controls to block data exfiltration and malware injection across application traffic. In explicit proxy mode you can set up filters in just a few clicks to control outbound requests and the responses your applications receive, protect against domain or SNI spoofing, and restrict access to trusted domains or IPs. The service supports TLS inspection and granular HTTP header filtering, and emits detailed logs to Amazon S3 and AWS CloudWatch. Preview access is free in US East (Ohio).

🔒 DevOps platforms like GitHub, GitLab, Bitbucket, and Azure DevOps accelerate development but also introduce data risks from misconfigurations, exposed credentials, and service outages. Under the SaaS shared responsibility model, customers retain liability for protecting repository data and must enforce MFA, RBAC, and tested backups. Third-party immutable backups and left-shifted security practices are recommended to mitigate ransomware, insider threats, and accidental deletions.

🔒 Cameyo by Google is a cloud-native Virtual App Delivery solution that streams legacy Windows and Linux applications into the browser or publishes them as Progressive Web Apps, avoiding the overhead of full VDI. Paired with Chrome Enterprise Premium, Cameyo brings legacy client apps under a single secure browsing context with advanced DLP and threat protection. IT teams benefit from faster deployments, reduced VPN and infrastructure complexity, and a clear migration path to ChromeOS while preserving critical Windows workloads.

🛡️ Cybersecurity Awareness Month underscores that protecting organizational data requires attention to internal handling as well as external threats. Fortinet’s 2025 Insider Risk Report found 77% of organizations experienced insider-related data loss in the past 18 months, with nearly half of incidents tied to simple negligence. The report highlights mounting GenAI concerns and recommends a layered approach combining visibility, behavioral analytics, and real-time coaching to prevent accidental and malicious loss.

🔍 The 2025 Insider Risk Report finds insider-driven data loss is widespread and costly, with 77% of organizations affected and many incidents stemming from human error or compromised accounts rather than malice. It warns that traditional DLP often lacks behavioral context and visibility across endpoints, SaaS, and GenAI. The report urges adoption of behavior-aware, AI-ready platforms and five practical practices to reduce false positives and prevent data loss.

🔒 This buyer’s guide explains why organizations need comprehensive data protection platforms for hybrid cloud environments and which capabilities to prioritize. It highlights core requirements such as data discovery and classification, layered protections (encryption, DLP, immutability), continuous monitoring, and automated recovery to address ransomware, misconfigurations, outages and compliance. The guide also surveys market trends and leading vendors to help IT teams evaluate DPaaS, cloud-native and on-premises options.

🔒 The Forrester Total Economic Impact™ study commissioned by Microsoft found that Microsoft Purview reduced the likelihood of data breaches by 30% for a composite organization, yielding more than $225,000 in annual savings from avoided incidents and fines. The report credits unified governance, automated classification, and fine‑tuned DLP policies with a 75% reduction in investigation time and 75% time savings for users searching and classifying data. Over three years the study shows $3.0M in benefits versus $633,000 in costs (NPV $2.3M; ROI 355%).