Security | Threat Detection | Cyberattacks | DevSecOps | Compliance

Most vendor onboarding and app access work is waiting and follow-ups. Waiting for someone to notice a form came in, assign a tier, chase a questionnaire, or dig up the context behind a Slack request. Risk Automations workflows remove that wait and automate the follow-up. A trigger fires, and the workflow runs to a concrete outcome: a ticket created, a message sent, a risk tier assigned. To make those workflows easier to launch, Risk Automations includes an ever-expanding template library.

You approve five AI tools; your employees use 20. According to UpGuard's State of Shadow AI report, 81% of the workforce is already bringing unmonitored AI tools to work, and legacy security tools are leaving massive gaps in workforce Shadow AI and regulatory compliance. Modern AI governance platforms give you real-time visibility and runtime guardrails to close that gap. They back it up with automated auditing, so you have evidence when someone asks for it.

Most teams shopping for digital risk protection solutions already run three tools at once: one for brand monitoring, one for dark web monitoring, and another for social media defense. The signals don't line up, the alerts pile up, and there’s no single view to show what's exposed. Attackers keep wearing a trusted brand's face, which is why fragmentation matters.

Vendor assurance efforts are increasing, but risk leaders don’t trust the results of that effort. In KPMG’s Global Third-Party Risk Management (TPRM) Survey, only 15% of risk leaders said they have high confidence in the data that underpins their TPRM program. Only 17% rate their data quality as excellent. Security teams are running more assessments and sending more questionnaires than ever, but fewer than one in five leaders trust what any of that produces.

“The call is coming from inside the house.” It’s one of horror’s oldest lines, and you already know how the scene goes. The team scrambles, rechecks every firewall, audits every login, hunting for an intruder. Then the trace comes back, and there isn’t one because there is no malware or forced entry. Just an employee, at their own desk, with their own login, who pasted a confidential spreadsheet into an unapproved AI tool to save 10 minutes before a deadline.

Oracle's July 2026 Critical Patch Update (CPU) is nearly three times larger than any release in the company's history. To understand it, we parsed all 23 of Oracle's quarterly advisories going back to 2021, matched them against the official CVE record, and compared Oracle against eight other major vendors. We set out to answer three questions: How much of this is genuinely new? Does it really reflect AI-accelerated patching? And how unusual is it?

The modern security landscape is an unfair fight. The perimeter is gone—replaced by a borderless terrain where a team of one to 10 is expected to defend the same footprint as a 50-person security operations center (SOC). Attack surface. Vendor ecosystem. Workforce identity. Even fully-staffed teams struggle to cover them all. For a lean team, that coverage fractures almost instantly.

Confidential and sensitive data moves across the dark web every second of every day, and that stolen data has become a reliable fuel source for breaches. In 2025, reports from Cybernews revealed that researchers had uncovered roughly 16 billion exposed credentials and that artificial intelligence (AI) now accelerates what attackers can do with that data once they have it.

Organizations keep their databases behind firewalls for a reason: the data inside is the data they can least afford to lose. A new class of AI middleware–Model Context Protocol (MCP) servers–exists specifically to reach into those protected systems on an AI model's behalf. One of them, DBHub, connects directly to SQL databases.

Shadow IT used to mean employees spinning up unsanctioned software-as-a-service (SaaS) apps that stored company data without approval. Today, shadow MCP and unsanctioned LLM integrations represent the next evolution, and they're more dangerous. Model context protocol (MCP) servers don't merely store data; they act on it, executing code, calling APIs, and accessing internal tools on behalf of AI agents that developers connect with a config file.