Security | Threat Detection | Cyberattacks | DevSecOps | Compliance

Most teams that assess cloud vendors already have a general idea of the Consensus Assessment Initiative Questionnaire (CAIQ) and Cloud Controls Matrix (CCM). However, you may not have a good answer for what it takes to run that assessment. Turning a vendor's trust center page, SOC 2 report, and security policy into a structured, defensible view of CCM control coverage is a different problem entirely.

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.