A critical finding can move through several security tools and still leave the organization exposed.  The vulnerability has a severity score, and a ticket has reached the queue. Progress then depends on information the finding may lack: who owns the system, whether attackers can reach it, what protection exists, and how a change could affect the business.  Each missing detail creates

October 2026 brought another wave of cyber threats targeting US businesses, with attackers exploiting trusted platforms, legitimate authentication processes, and everyday business workflows. For enterprises, these campaigns raise the stakes beyond individual security incidents, increasing the risk of financial losses, data breaches, and costly operational disruption. Below, we examine five

Your team is remediating more vulnerabilities than ever, yet your breach risk has barely changed. You’re spending too much time and effort on exposures attackers may never exploit, while higher-risk exposures sit, waiting in the queue. Gartner®’s report on exposure prioritization states that fewer than 10% of vulnerabilities are exploited, yet most are treated as urgent. The report also argues

Forty percent of security teams now use AI every day, and another 56% are testing it. Only 4% have no plans to adopt it, according to Prophet Security's State of AI in Security Operations 2026 survey of more than 250 CISOs, SOC leaders and practitioners. For the teams still testing, the next decision is whether their own SOC is ready. AI SOC readiness has two parts: whether a SOC has the

The alert is critical. The fix is known. And the ticket sits in a queue for days. Security teams understand the risk, but the people who keep applications available need proof that the change won’t break anything.  Their concerns are practical. An IPS protection could block legitimate traffic. A configuration change could interrupt a service. An endpoint policy could affect an application

Security teams have spent years trying to detect threats faster. AI is changing the harder part: how much time defenders have left to act. Advanced AI models can now help attackers discover vulnerabilities, generate exploit code, and move through weaknesses faster than traditional security processes were built to handle. The challenge is no longer just finding another vulnerability or

AI is beginning to take over much of the work that has traditionally defined the security analyst's job. Investigating alerts, collecting evidence, correlating activity across tools, and executing response actions are becoming tasks that AI systems can perform on their own. As these capabilities mature, security operations centers (SOCs) will see major changes in how work is organized, how

Artificial Intelligence (AI) has become one of this decade's defining technologies. From healthcare and finance to manufacturing and education, organizations increasingly rely on AI to automate repetitive tasks, uncover patterns hidden within large datasets, and support faster decision-making. Cybersecurity has experienced a similar transformation. While attackers employ AI to automate

Modern phishing campaigns can compromise American businesses without relying on traditional credential theft. Attackers can abuse legitimate cloud services, authentication mechanisms, and trusted business workflows to gain access to corporate accounts while leaving fewer obvious signs behind. ANY.RUN's analysis of Skyw4lker, a device code phishing campaign primarily targeting U.S. organizations

Fake login pages are nothing new, but generative AI is making them easier to build and more convincing. Attackers can recreate familiar login screens, impersonate trusted brands and tailor phishing attempts to specific employees or organizations.  Some fake login pages go beyond stealing passwords. They can also capture MFA codes or hijack authenticated sessions, allowing attackers to gain

Every SOC struggles with a pile of alerts. Very few of those alerts explain how an attack started or how it progressed. Network data, like that from a network detection and response (NDR) platform, connects individual alerts into one timeline, and it feeds the defensive systems that can tie all events together into one coordinated view. SOC teams often first see the shape of an intrusion

Right now, there is probably an AI agent running on a laptop somewhere in your network. It stores API keys and database passwords, can access your internal systems, and works without anyone watching. Nobody reviewed its access. Nobody owns it. If an employee had that much reach with that little oversight, you would call it an insider risk. So why do we still call the agent a productivity tool?

Platform-as-a-Service (PaaS) provider Railway on Wednesday disclosed "anomalous activity" related to systems that store customer environment variables. Upon further investigation, the company found that an unauthorized third party may have gained access to encrypted environment variables, and, in some cases, obtained the decrypted contents. The impact, it said, is limited to a "subset of users,

Generative AI has changed what a malicious email looks like. Messages that once gave themselves away through clumsy wording, generic pretexts, and reused templates now arrive fluent, personalized, and unique to each recipient, and the behavioral signals email filters have relied on for years are fading as a result. Proofpoint shared several examples of the shift at its Protect 2026 conference.

An AppSec team can point an AI agent running on a general-purpose model at its own web apps with little setup. Sure, the model writes the attacks. But everything that makes those attacks safe to run against production and worth acting on afterward lives in the harness around the model. The harness is where the engineering in AI pentesting sits. The Hugging Face intrusion OpenAI disclosed in July

A sales manager builds an AI-assisted workflow to route leads. It connects to customer records, summarizes inbound requests, and posts updates to a shared channel. The workflow saves time, reduces manual work, and soon becomes part of the team’s daily routine. Across the same organization, employees are building useful apps, agents, and automations faster than existing processes can support them

In Salesforce Agentforce, the recommended way to make an agent respond to events is a Flow: a record is created or a case is updated, and the Flow passes data into the agent's prompt. Separately, Email-to-Case and Web-to-Case turn inbound customer messages into case records; it is how airlines and hotels run support intake. Both are standard, documented, as-designed features. Chained together,

In the space of a month, a new category of AI model went from nonexistent to everywhere. TypeSafe AI released Jev on September 15 and called it a System One model, a nod to Kahneman's fast, intuitive thinking. OpenAI announced its own Decisions API at DevDay and opened a public beta on October 6. Cribl published cribl-decision, an open LoRA adapter you can run locally. Others, like Laya, are

Attackers are impersonating popular AI products to distribute malware, steal browser sessions, and compromise business accounts. ANY.RUN linked 1,258 public sandbox analyses to fake AI campaigns between April and October of 2026.  The campaigns also increase operational pressure on MSSPs serving US businesses, which accounted for 16% of country-attributed targets.  Similar attacks

AI is already part of digital forensics and incident response work, with or without a policy in place to govern it. The issue is that these tools are growing more confident in their own output faster than practitioners are developing reliable ways to verify it. Without governance models and validation standards in place, that gap compounds and rarely stays contained to a workstation: an