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Week 4 CYBR 325

  Google Gemini and the Risks of Agentic AI A recent report about Google’s Gemini AI highlights both the potential and the risks of increasingly autonomous artificial intelligence. During a cybersecurity test in May 2026, a Gemini system accessed the computer systems of three real companies even though the test was supposed to involve fictional targets. Google said the AI stopped after recognizing that it had reached real organizations and that no damage was reported. What makes this story especially interesting is that Gemini was being tested for cybersecurity capabilities. In the process, it was able to use information available online and, in some cases, gain access to protected systems. The incident appears to have resulted from an unintended internet-access problem in the testing environment. Similar AI security-testing incidents have also involved other major AI companies, showing that controlling increasingly autonomous AI systems is becoming an important challenge. This sto...
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Week 3 CYBR 325

  The Business Insider article discusses how Ukrainian and U.S. developers are using artificial intelligence to help soldiers treat battlefield injuries when a medic or medical evacuation is not immediately available. One tool, called ALICE , uses Google’s Gemini AI to analyze photos of injuries, ask follow-up questions, and provide guidance based on battlefield medical protocols. Another tool, FieldCare GPT , is being developed by the University of Pittsburgh and works offline, allowing soldiers to access medical information even when internet or radio communication is unavailable. These AI tools are designed to assist soldiers with important decisions, such as controlling severe bleeding or managing other battlefield injuries. However, the article emphasizes that AI is not intended to replace trained medical personnel . There are still concerns about the accuracy of AI-generated medical advice and whether inexperienced users might misunderstand the guidance. Overall, the article...
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Week 2 CYBR 325

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    After our zoom class, I came across this article from IBM. I learned that machine learning allows computers to learn from data and identify patterns without being specifically programmed for every situation. This process is what allows many of the AI technologies we use every day to make predictions and decisions. Most people are familiar with AI, but many may not know that machine learning is an important part of modern AI. I wanted to understand the distinction between the two so that I have a strong foundation as I continue learning about this topic. When most people hear the word AI, they probably think of ChatGPT. But AI is a much broader term that refers to a computer system’s ability to perform tasks that normally require human intelligence. One thing that stood out to me is how important data is to the machine learning process. If a model is trained using inaccurate or incomplete data, the results it produces may also be inaccurate. Their performance depends heavi...
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Week 1 CYBR 325

  This week was my first refresher and introduction to real-world applications of artificial intelligence and machine learning in cybersecurity. Before researching the topic, I mainly thought AI was used to automate tasks. While working and researching on the career path assignment I learned that its uses are much broader and can help cybersecurity professionals detect threats, analyze data, and respond to attacks. One application I found interesting was Assala Energy’s use of an AI-powered SOC assistant. The system helps analysts review and prioritize security alerts by identifying suspicious activity and escalating important events. This can reduce alert fatigue and allow analysts to focus on more serious threats. I was also interested in Visa’s use of AI and machine learning to detect financial fraud. The system analyzes transactions in real time using factors such as spending patterns, locations, devices, and previous fraud trends. This allows Visa to analyze huge amounts of da...
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Week 10: Cybercrime dilemma

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  Cybercrime—ranging from ransomware to online fraud—is inflicting hundreds of billions of dollars in losses across the U.S., threatening individuals, schools, businesses, utilities, and governments . Ransomware attacks on K–12 schools have disrupted instruction and cost districts up to $ 1 million, while the U.S. Marshals Service itself fell victim, its systems compromised and sensitive data accessed . Although federal agencies such as the FBI, Secret Service, and DEA deploy various tools—from public complaint portals to investigative databases—the lack of a uniform definition of “cybercrime” , the absence of a centralized federal data repository , and reluctance by victims (especially businesses) to report incidents—makes it impossible to grasp the full extent of the problem . To address these gaps, Congress enacted the Better Cybercrime Metrics Act in 2022, mandating the DOJ to create standardized categories of cybercrime and integrate them into the FBI’s National Incident...
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Week 9: Vulnerability Management

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   Vulnerability management is a continuous process that identifies, evaluates, treats, and reports security vulnerabilities across systems and software. It helps reduce the attack surface and ensure compliance. The process includes asset discovery, vulnerability scanning, risk prioritization, remediation, and verification. Automation and risk-based tools enhance its efficiency, making it essential for modern cybersecurity strategies. Key Stages (Lifecycle) Asset Discovery Identify and catalog all IT assets—servers, endpoints, apps, networks—which forms the foundation for vulnerability scanning. Vulnerability Scanning Use automated tools to detect known flaws such as misconfigurations, open ports, and out-of-date software across the infrastructure . Prioritization & Risk Rating Assign risk levels (e.g., CVSS scores, severity ratings) based on likelihood, business impact, exploitability, and compliance requirements .. Remediation & Mitigation Apply patches,...
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Week 8: Identity-based Attacks Breaching Retail

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  Exploiting Third‑Party Trust (Adidas) Attackers gained access through a vendor’s SaaS integration using stale tokens/service accounts that lacked MFA or expiration. This supply-chain style breach exposed customer data without any malware—just unchecked trust in third parties   Credential Stuffing & Privilege Abuse (The North Face) Using leaked username/password combos, attackers accessed customer accounts via credential stuffing. With no MFA protection, they quietly extracted PII—demonstrating how weak identity hygiene opens doors.    SIM Swapping & Social Engineering (Marks & Spencer, Co‑op) Threat group Scattered Spider tricked help desk staff using SIM swap and impersonation, resetting MFA and password protections. This kind of human-layer trickery granted lateral access through overprivileged or dormant SaaS identities   Hijacked Unmonitored SaaS Admins (Victoria’s Secret) Although details are sparse, attackers likely targeted high-pr...
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