The Hacker News | Expert Insights

Check your expanding identity attack surface Identity has become the new perimeter, and attackers know it. According to new research from CrowdStrike, 80% of modern cyberattacks are identity-driven , leveraging compromised credentials.  This substantial volume is driven by the widespread use of well-known directories such as Active Directory, and the vulnerabilities associated with compromised privileged accounts that are overprivileged or unmanaged, not network intrusion.  The explosion of privileged credentials and lack of adequate visibility or oversight can be attributed to growth, whether due to migration to the cloud, mergers and acquisition, the increased use of contractors, or simple organic business growth. This growth outpaces the ability to adequately govern the environment, leaving exploitable vulnerabilities. Vulnerabilities hiding in plain sight Your IAM environment may be loaded with vulnerabilities because of unchecked privileged accounts. The prim...

Most security teams know that dwell time matters. The harder question is what to do about it. Dwell time is the period between an attacker gaining access and the security team containing the threat. During that window, a threat actor has time to learn the environment, steal credentials, move between systems, and reach sensitive data. For years, security teams have tried to reduce this window by adding more detection tools. The problem is that more alerts do not necessarily mean faster detection.  A recent industry incident response report puts the global median dwell time at 14 days, up from 11 the year before, quietly reversing a run of steady improvement that had held for close to a decade. The better way to think about it is as an operational problem. Two numbers matter most. Mean time to detect (MTTD) tells you how quickly the team recognizes a real threat, while mean time to respond (MTTR) tells you how quickly the team investigates and contains it. An AI native SIEM...

As organizations deploy AI coding agents across large monorepos and microservices environments, a fundamental problem emerges: the model may be capable of making the change, yet still lack the organizational context required to make the right change safely. A developer can ask an AI coding agent to deprecate an API field, update an authentication flow, or modify a service interface. The agent can inspect the code available on the developer's machine and search for references. What it may not know is that the field is consumed by four other services across separate repositories, that one of those services belongs to another team, or that the same field eventually carries sensitive data into a third party integration. This is not simply a context window problem. It is a code context problem: providing AI agents with accurate, current, organization wide evidence about how software actually behaves. One emerging approach is to generate that evidence directly from source code us...

AI coding agents are already inside engineering organizations. The problem security teams need to solve is not only that AI-generated code might be vulnerable. You already have ways to catch that: code review, CI, SAST, dependency scanning, and production monitoring. The real problem is that tools such as Claude Code, OpenAI Codex, Claude Cowork, and GitHub Copilot are becoming extensible agent runtimes. Skills, plugins, hooks, repository instructions, and MCP servers can influence what the agent reads, which tools it selects, what commands it runs, and where enterprise data is sent. Most AI governance programs stop at approving the application. Very few can tell you everything that has been installed inside it. That is the supply-chain gap. What changed: Third-party components are no longer participating only at build or deploy. They are participating in the agent’s decision loop. From coding assistant to agent runtime The first generation of coding assistants mainly...