Security | Threat Detection | Cyberattacks | DevSecOps | Compliance

TITAN AI Demo Series: How to Send Vendor Questionnaires with TITAN Assess

Vendor questionnaires out the door faster. Responses back sooner. No manual coordination required. In this week's edition of SecurityScorecard Demo Tuesdays, see how TITAN Assess streamlines the entire questionnaire outreach process — so your team spends less time on admin and more time acting on what vendors actually tell you.

Best AI Governance Platforms for Enterprises: Top 6 in 2026

AI governance platforms provide enterprises with centralized oversight to manage AI risks, ensure regulatory compliance, and automate policy enforcement across the AI lifecycle. Leading solutions include security-oriented tools like Mend.io, HiddenLayer, and Prompt Security, as well as end-to-end governance platforms like IBM watsonx.governance and Microsoft Purview.

Safeguard: Using the double-edged sword of AI for good

The concept of AI might trigger both excitement and stress for those who spend all their time either using it for efficiency or fighting it as it tries to breach their systems. A massive force of non-human identities that have been summoned to expose the tiniest cracks in an organization’s security is enough to overwhelm any IT team. However savvy, modern security professionals have begun to raise their own versions of AI armies – designed to stop those sent by malicious actors.

Bringing Real-World Cyber Events Directly Into the Cyber Risk Register

Kovrr's cyber risk quantification (CRQ) models are built on a continuously updated database of real-world cyber events, drawing on regulatory disclosures, company filings, legal reports, and proprietary insurance claim intelligence to produce financial exposure estimates grounded in how incidents actually unfold. That intelligence foundation has always informed everything the platform produces, from frequency and severity calculations to the event catalogs that drive each organization's quantification.

CrowdStrike Uncovers New Prompt Injection Techniques

Prompt injection is among the defining security challenges of the AI era. As organizations move from chatbots to AI agents, adversaries are finding more ways to manipulate the language, context, and data these systems trust. With the rise of powerful AI agents that can crawl webpages, access file stores, and even write shell commands, indirect prompt injection has emerged as a critical threat vector.

How to Build an AI Asset Inventory

Most organizations that have invested in AI governance have done so without first solving the problem that makes governance possible in the first place: knowing what AI they are actually running. An AI governance program built on an incomplete inventory is governing a partial picture of actual exposure. ‍ The risks concentrated in the AI systems that never made it into the formal catalog are not lower priority because they were not captured. They are simply invisible, which is considerably worse.

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.

IT Audit: What It Is and How to Prepare for One

Cramming for an exam in school often meant late nights, large quantities of caffeine, and anxiety about potential final grades. Whether studying alone or as part of a group, you probably tried to pull together all your notes and review sheets, so you had the right information at your fingertips during the test. In some cases, these exams could determine whether you passed or failed a course.

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.