Security | Threat Detection | Cyberattacks | DevSecOps | Compliance

Microsoft 365 E7 and the Rise of AI Agents: What Security Leaders Need to Know

For years, enterprise security focused on protecting users, endpoints, applications and data. Today another identity is entering the enterprise. AI agents. Unlike traditional chatbots that simply answer questions, modern AI agents can perform tasks on behalf of users. They can search corporate knowledge, summarize documents, create reports, interact with business applications and, with appropriate permissions, execute multi-step workflows.

Your AI Agent Could Leak Enterprise Data #Shorts #aiagents

AI agents don't just answer questions—they access enterprise data, call APIs, interact with MCP servers, and trigger workflows. That means sensitive information like PII, PHI, HR records, pricing data, financial information, and confidential business data can flow through AI systems. In this YouTube Short, Amar Kanagaraj explains why AI governance, data security, and data sovereignty are essential for enterprise AI deployments—and how the NetScaler × Protecto integration helps organizations secure AI workflows.

7 Hidden Risks of AI in the Workplace

Is your team using AI tools at work? Without the right guardrails, you could be exposing your business to data breaches, compliance violations, and serious reputational damage — and most companies don't see it coming. In this video, we break down the 7 hidden risks of AI in the workplace — from data privacy breaches and AI hallucinations to Shadow AI, prompt injection attacks, and intellectual property complications. We also cover the best practices every organization needs to manage workplace AI risks before they become costly problems.

Build Agents, Automate Workflows, and Unlock Your Content-All in One Platform

88% of organizations are running AI in at least one workflow, yet nearly two-thirds report more rework than savings. The model is rarely the bottleneck. Everyone has access to the same frontier models now. The difference is what sits underneath: content that's unstructured, ungoverned, and disconnected from the workflows that need it. Fixing the content problem usually means giving AI broad access to content, and that's where governance breaks down.

Called it (mostly): Checking in on 2026 predictions so far

On this episode of Masters of Data, we revisit the predictions Adam White, Zoe Hawkins, and David Girvin made at the end of last year, checking our own scorecard halfway through 2026. The hits: agents running amok and deleting databases, MCP becoming the backbone for tracking what agents actually do, growing security gaps around personal data, and a collective rejection of low-quality AI content. The misses: we underestimated how fast companies would cut staff for AI, then quietly start rehiring once the agents couldn't cover the work, and we're still arguing about whether token burn is a cost problem or a coming attack vector.

Implementing AI Security: Your Enterprise LLM Security Checklist

Security teams are approving large language model (LLM) deployments faster than they can build the controls necessary to govern them and protect vital, sensitive data. Employees paste customer records into ChatGPT, engineering teams connect internal APIs to coding assistants, and business units stand up retrieval systems against production data, often without formal review.
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The biggest security risks facing financial institutions in 2026

Financial institutions are spending more on security than they were five years ago. They have more security tools, invest more in training, have more policies in place and report on security more regularly. That sounds positive, but it does not automatically make them more secure. One of the biggest challenges for security leaders is deciding where to focus. New vulnerabilities, threat reports and regulatory requirements appear all the time. With so much competing for attention, it can be difficult to separate genuine priorities from the latest headline.

GLM 5.2 Signals a New Phase of Accessible Frontier AI and a Shift in Cyber Risk

AI’s latest wave is reshaping cybersecurity in a fundamental way. Capabilities that once were limited to a handful of frontier models are now widely accessible, cheaper, and embedded across more environments. As access expands, risk is growing fast and scaling even faster.

Reduce SAST false positives with agentic evaluation and Bits Memories

Static application security testing (SAST) tools are intentionally conservative. Traditional scanners identify code that appears exploitable and flag the snippet for review, even when protections elsewhere in the application prevent exploitation. Although that approach helps teams catch vulnerabilities, it also creates false positives that consume developer time, slow remediation efforts, and make future alerts easier to dismiss.