Security | Threat Detection | Cyberattacks | DevSecOps | Compliance

In our previous two posts, The ABC’s of Ishing and From Lure to Breach, we broke down the foundational tactics used by cybercriminals to deceive users and gain unauthorized access. This follow-up report expands on that foundation by exploring three evolving phishing threats that go beyond traditional email lures: clone phishing, deepfake phishing, and Captcha phishing.

Model Context Protocol (MCP) servers facilitate the integration of third-party services with AI applications, but these benefits come with significant risks. If a trusted MCP server is hijacked or spoofed by an attacker, it becomes a dangerous vector for prompt injection and other malicious activities. One way attackers infiltrate software supply chains is through brand impersonation, also known as typosquatting—creating malicious resources that closely resemble trusted ones.

Most large organizations rely on multiple vulnerability and exposure scanning tools out of necessity. Infrastructure scanners, cloud security platforms, application security testing tools, container scanners, and attack surface management solutions all play a role. Each one is designed to answer a specific question. But when it comes to understanding the risk of the vulnerabilities and exposures they detect, each tool has its own approach to quantifying it.

In the first round of the AI gold rush, most conversations about AI security centered on models: large language models, training data, hallucinations, and prompt safety. That focus made sense when AI was largely confined to generating text, images, or recommendations. But that era is already giving way to something far more consequential.

‍Artificial intelligence (AI) systems and generative AI (GenAI) tools have already been embedded across enterprise operations in a myriad of ways that trigger compliance obligations, both in terms of AI-specific regulations and other reporting mandates. In many cases, this adoption is occurring informally, through employee-driven tools or AI features embedded within third-party platforms, without centralized visibility or approval.

Open source is one of the best things to ever happen to software development. It is also one of the easiest ways to accidentally ship legal obligations you did not sign up for. Most teams know they rely heavily on open-source dependencies. Fewer teams know exactly what licenses those dependencies use, what obligations come with them, or how those licenses travel through transitive dependencies and container images. That gap is what we call open-source license risk.

Exactly twelve years from now, on January 19, 2038, at 03:14:08 UTC an unpredictable amount of computer systems will think they have been teleported in time more than 100 years, back to December 13, 1901.