Security | Threat Detection | Cyberattacks | DevSecOps | Compliance

Risk management maturity model: How to assess and improve your program

Accelerating security solutions for small businesses‍ Tagore offers strategic services to small businesses. A partnership that can scale‍ Tagore prioritized finding a managed compliance partner with an established product, dedicated support team, and rapid release rate. Standing out from competitors‍ Tagore's partnership with Vanta enhances its strategic focus and deepens client value, creating differentiation in a competitive market.

The AI Inventory Problem Nobody Solved

By now, most organizations have invested in AI governance. Far fewer have solved the problem that makes governance possible in the first place: knowing what AI they are actually running — and with 57% of employees using AI tools at work without telling their manager, the gap is wider than most inventories admit. In this video, Kovrr breaks down what an AI asset inventory actually is, why traditional asset management never catches shadow AI, and what it takes to keep the record accurate.

How to Transform Cybersecurity Data Into Risk Metrics

Enterprise security teams sit on enormous volumes of operational data. Vulnerability scanners produce thousands of findings weekly. Endpoint agents generate millions of events daily. SIEM platforms ingest logs from every system in the environment. Threat intelligence feeds fire off indicators by the hour. All of this data is useful for operational security work.

How to Quantify Cyber Risk Effectively: A Practical Enterprise Guide

Effective cyber risk quantification means moving past subjective heatmaps and translating technical vulnerabilities into dollar-denominated loss exposure and probability distributions that the CFO, board, and cyber insurance underwriter can act on. It is the discipline that turns cyber from a technical cost center into a strategic risk portfolio managed alongside every other category of enterprise exposure.

AI Agent Sprawl and How Enterprises Are Controlling It

AI agent sprawl is the uncontrolled proliferation of AI agents, autonomous assistants, and LLM-powered tools across an organization without centralized tracking or governance. It mirrors historical IT challenges like SaaS sprawl and shadow IT, and it emerges when decentralized business units build or deploy agents independently, without coordinated oversight from security, IT, or risk teams. ‍ The difference is that these agents are active software actors.

AI Agent Governance: How Enterprises Should Approach It

Governing AI agents at enterprise scale requires a fundamental change in how security, risk, and compliance teams think about AI oversight. The generative AI era focused governance on output quality: what the model says, what it produces, and whether the content meets policy standards. ‍ The agentic era demands governance of action and delegated authority: what the AI is allowed to do, what systems it can touch, and how its decisions trace back to human accountability.

TITAN AI Demo Series: Query Vendor Risk Within Claude

Your security team already lives in Claude. Now TITAN AI does too. SecurityScorecard's new Model Context Protocol (MCP) connector brings TITAN AI directly into Claude. Your team can query vendor risk, scores, and findings without leaving the tool they already use every day. No new dashboard, no extra login, just answers where the work already happens. In this episode of SecurityScorecard's Demo Tuesday series, see the TITAN AI MCP connector in action inside Claude.

The Illusion of AI Containment: Why AI Guardrails Won't Save Your Supply Chain

AI is quickly becoming one of the most useful tools available to security researchers. Its ability to analyze enormous volumes of data, identify vulnerabilities, reconstruct attacks, connect seemingly unrelated signals, and help defenders respond faster than humans could alone is incredibly beneficial.