Security | Threat Detection | Cyberattacks | DevSecOps | Compliance

A developer on your team just told Claude Code to connect to a new MCP server, the protocol coding agents use to reach organizational tools and data. Nobody in security reviewed it. Nobody in security even knows it happened. For two-thirds of enterprises, the primary obstacle to scaling agentic development isn’t budget or headcount — it’s security risk.

Coding agents have a misconfiguration problem. YOLO mode enabled to reduce approval friction, sandbox enforcement disabled, Model Context Protocol (MCP) servers installed from public marketplaces without security review. These are common configurations in enterprise developer environments, and remediating them is genuinely valuable work. AI Security Posture Management (AISPM) addresses exactly this class of risk, surfacing misconfigurations before they create the conditions for a successful attack.

AI agent authentication is the process of verifying that an autonomous agent is the identity it claims to be before it interacts with infrastructure, applications, APIs, or data. Because agents often act on behalf of users, services, or workflows, authentication must be paired with delegated context and downstream authorization controls that determine what the agent is allowed to do, which resource it can access, and how long that access should last.

AI is compressing the cybersecurity timeline faster than most institutions can adapt to it. Not only do security leaders have to deal with this new reality, they have to answer key questions to internal and external audiences about their efforts — including regulators.

“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.

According to new research from Cyberhaven Labs, drawn from real-world data movement events and active user counts across Cyberhaven's enterprise customer base between December 2025 and June 2026, Claude Desktop usage grew 1,233% between January and June 2026.

Prompt injection in a standard LLM interaction produces bad output. The model says something it shouldn’t. The damage stays contained to text. Prompt injection in an MCP environment is a different problem. Agents built on the Model Context Protocol don’t just generate responses. They call tools. They write files, query databases, send emails, execute code, invoke APIs.

Generative AI has quickly moved from experimentation to everyday business use. Employees use AI tools to create content, write code, analyze data, automate repetitive tasks, and interact with business applications in entirely new ways. At the same time, software vendors are embedding AI capabilities into the products organizations already use every day, often without requiring users to adopt separate tools.