LevelBlue is proud to announce that Cyber Defense Magazine has named it the winner of its Global InfoSec Awards 2026 for Trailblazing Managed Security Service Provider (MSSP) and Market Disruptor Threat Detection, Incident Response, Hunting and Triage Platform.

Non-human identities (NHIs) and AI Agents including service accounts, CI/CD credentials and cloud workload identities, now eclipse human identities in enterprise identity systems by 50:1 to 100:1. Modern identity security platforms must assign identities to these assets and furthermore, apply roles, access control policies, visibility and governance in order to secure the modern enterprise.

Jira Cloud APIs are widely used for automation and integrations across CI/CD, DevOps, reporting, and internal tools. Atlassian provides native REST API authentication using API tokens and OAuth. This works well for simple scripts and internal automation. However, modern organizations often require stronger controls when APIs are used by multiple services, integrations, and automated systems. As integrations grow, teams often need a more controlled authentication model than user-based tokens alone.

As organizations race to adopt new AI tools, deploy AI agents, and build AI-powered software, they create new attack surfaces that traditional security controls were never designed to protect. A key example is the prompt and agentic interaction layer, which faces novel threats like indirect prompt injection and agentic tool chain attacks.

In most organizations, the largest line item on the balance sheet isn’t technology, it’s people. Labor accounts for 70% of total operating expenses in most SaaS businesses, yet executives often lack a clear view of how that investment translates into productivity, risk exposure, software utilization, and operational efficiency. At the same time, organizations are facing a new wave of hidden costs: These blind spots quietly drain millions from operating budgets.

There’s a claim gaining traction in the market: homomorphic encryption can preserve data privacy in AI workflows. Encrypt your data, run it through a language model, and never expose a single token. Sounds bulletproof. It isn’t. Homomorphic encryption (HE) was built for math, not language. Applying it to LLM pipelines is like encrypting a book and asking someone to summarize it without reading a word. The problem isn’t efficiency.