Security | Threat Detection | Cyberattacks | DevSecOps | Compliance

AI tools have become a workplace staple, but their unsanctioned use has given rise to shadow AI. It refers to the untracked usage of any AI tools or applications without approval or overview of the information technology (IT) or security team. This is substantiated by the Verizon 2026 Data Breach Investigations Report (DBIR) which shows how rapidly the shadow AI trend is growing.

You can stand up a vault in two weeks. The team at One Identity says that’s about how long it takes to get the basics running, from discovery scans to approval workflows. Two to four weeks of work, and a respectable PAM foundation is in place. That’s not the hard part. The hard part starts the next morning, when actual users start working around the system.

This CISO Executive Briefing analyzes material developments over two horizons: the past week (July 1–7, 2026) and the past month (June 8–July 7, 2026). Analysis draws exclusively from verified public disclosures, regulatory filings, threat intelligence platforms, and incident reporting. Focus areas include AppSec posture, software supply chain integrity, identity and contractor risk, cloud/IaC exposure, and the accelerating integration of AI into attacker TTPs.

Every security leader can describe their network security architecture in confident detail: how traffic should flow, where segmentation boundaries sit, which access should never be permitted. What almost none can tell me with certainty is whether their live controls are actually enforcing that design right now, at this very hour. That gap between what we intend and what is actually running in production is where modern breaches live, and it widens with every change we make.

AI agents now retrieve data, generate recommendations, and trigger actions across enterprise systems with little human review in between. That speed is the point, and it is also the problem. A single manipulated prompt or a poisoned data source can push an AI system toward a decision no one signed off on, and most security teams have never tested for it. Building a red team exercise for AI workflows is how you find that gap before an attacker does.

On July 7, 2026, the European Central Bank sent a letter to the CEO of every bank it directly supervises with an unambiguous instruction: build a formal action plan against AI-enabled cyberattacks, and submit it to your supervisory team by October 31.

The browser has become the enterprise workspace. Employees, contractors, partners, and third parties use browsers to access SaaS applications, internal web apps, admin consoles, collaboration tools, and AI services from anywhere, often across a mix of managed, unmanaged, and personally owned devices. As they do, adversaries are increasingly targeting the browser session itself.

Managing an artificial intelligence (AI) footprint has emerged as one of the most complex challenges for modern enterprise security and risk teams. As shadow AI, autonomous agents, and embedded third-party models infiltrate corporate environments, traditional methods of software tracking have broken down. Organizations are quickly realizing that maintaining an accurate inventory is not just an IT best practice.

AI agents and LLMs are already building, analyzing, and deploying code across your software development lifecycle. As software supply chains become increasingly AI-driven, proactive security and access controls are your only path to success. To effectively govern authentication and permissions without sacrificing development speed, you must update your access management strategies.

Security teams are under constant pressure to move faster without giving up control, but in many organizations, access requests, approvals and credential workflows still live outside secure systems. They happen in chat messages, ticket comments or manual processes in tools like Slack, Teams, Jira or ServiceNow, increasing risk, reducing visibility and slowing response times.