Security | Threat Detection | Cyberattacks | DevSecOps | Compliance

Organizations continue investing heavily in cybersecurity tools, yet many security operations centers (SOCs) still struggle with alert fatigue, investigative delays, and inconsistent response outcomes. The issue is not necessarily a lack of technology. In many environments, it is the opposite. As security stacks expand, operational complexity often expands with them.

When the weather starts to get warmer, it is a sign that summer time is around the corner. But just as the weather heats up and travel plans get booked, scammers capitalize on the season by performing nefarious schemes to separate victims from their money and other valuables. Recent McAfee research found that more than one in three Americans have experienced a travel-related cyberthreat, with 41% of those affected losing money, often costing victims over $500.

Phishing attacks are evolving faster than traditional training programs can keep up. Advances in AI — including generative tools — are making attacks more dynamic, personalized, and harder to detect. At the same time, agentic AI for phishing security training is reshaping how programs improve, enabling them to adapt to user behavior and shifting risk in real time.

If everyone owns cybersecurity, no one really owns it.

An AI agent with access to a customer’s brokerage account can begin executing trades. Not because the customer asked. Because someone, somewhere upstream, slipped a hidden instruction into a tool the agent loaded at startup. The agent is doing exactly what it was told. Just not by the customer. This is not a hypothetical. It is the attack class that financial security teams have exactly zero legacy tooling to catch and it is arriving precisely as banks accelerate their agentic AI ambitions.

As cyber threats targeting critical infrastructure continue to evolve, the energy sector remains a prime target for malicious actors. Protecting the electric grid requires a strong regulatory framework and robust cybersecurity monitoring practices. In the United States, the Federal Energy Regulatory Commission (FERC) and the North American Electric Reliability Corporation (NERC) play key roles in safeguarding the power system against cyber risks.

Most vulnerability programs rely on scanning known assets and ranking findings based on static severity scores. That model breaks down quickly in modern environments. Asset lists are constantly changing, devices move between networks, workloads shift into cloud platforms, and unmanaged systems appear outside traditional inventory controls. When asset visibility is incomplete, vulnerability data is incomplete as well. The result is predictable. Prioritization becomes inconsistent.

Brand impersonation account takeover (ATO) happens when attackers use fake brand assets to expose customers, harvest credentials, and attempt access on the legitimate site. The impersonation stage happens outside the enterprise’s login environment, but the ATO risk appears when stolen credentials, attacker devices, or exposed users reach the legitimate login environment. That distinction matters because brand impersonation and account takeover are often handled as separate problems.

AI SOC memory is the difference between an investigation that starts from your team’s history and one that starts from zero. In most security operations centers, there is none: context disappears the moment a case closes. When a new alert fires, the investigation effectively starts from zero.

May 2026 will be remembered as the month the AI developer toolchain itself became the primary attack surface. A single threat actor — TeamPCP — ran a nine-day campaign that started as a worm in open-source packages, escalated through a poisoned code-editor extension, and ended inside GitHub’s own infrastructure.