Security Teams Need to Understand How Software Actually Gets Shipped

An AI assistant connected to internal data starts returning information it shouldn’t reveal. What does the incident responder collect first? Traditional evidence still matters, but the investigation may also require prompts, model outputs, retrieval sources, system instructions, access permissions and records of actions taken through connected tools. Disabling an endpoint won’t necessarily answer what the model accessed or what information it produced. Recent workforce data suggests many teams haven’t rehearsed that situation. ISACA’s 2026 State of Cybersecurity research, based on more than 1,800 cybersecurity professionals globally, found that only 8% of organizations regularly conduct AI specific response exercises. The same research found that 45% see LLM SecOps as a skills gap, up 12 percentage points from 2025 and 21 points from 2024. ISACA [https://www.isaca.org/about-us/newsroom/press-releases/2026/only-8-percent-of-organizations-global-enterprises-conduct-regular-ai-specific-response-exercises?utm_source=chatgpt.com] AI security is becoming operational work rather quickly. AI CHANGES THE EVIDENCE NIST convened an AI Incident Management Workshop in May because a new class of incidents is emerging as AI systems become embedded in critical infrastructure, cybersecurity and other operational environments. Its work covers incident definitions, lifecycles, taxonomies, existing playbooks

Organizations today face an increasingly complex threat landscape. New vulnerabilities emerge daily, attack surfaces expand continuously and attackers are leveraging automation to move faster than ever before. For many years, traditional penetration testing has been an essential part of cybersecurity programs. However, as environments become more dynamic, many organizations are exploring how artificial intelligence can enhance security assessments and provide more continuous visibility. This shift has given rise to AI penetration testing. But how does AI powered penetration testing compare to traditional penetration testing? Is AI replacing ethical hackers, or are the two approaches designed to work together? UNDERSTANDING TRADITIONAL PENETRATION TESTING Traditional penetration testing involves security professionals simulating real world attacks to identify vulnerabilities and weaknesses before attackers can exploit them. HOW TRADITIONAL PENETRATION TESTING WORKS A typical penetration testing engagement may include: * Reconnaissance and information gathering. * Vulnerability identification. * Exploitation and attack path analysis. * Privilege escalation testing. * Manual validation of findings. * Reporting and remediation recommendations. Traditional penetration testing provides deep insights into an organization's security posture

A cybersecurity exercise where everything goes according to plan is wonderfully reassuring. It may also be slightly suspicious. Real incidents rarely arrive with tidy instructions. An alert can look harmless until it isn’t. Evidence can contradict itself. Someone makes an assumption, spends 20 minutes following it and discovers they were looking in entirely the wrong place. That messy part of cybersecurity deserves a bigger role in how people are trained. NIST’s National Initiative for Cybersecurity Education (NICE) is putting that idea directly into its workforce discussion. Its upcoming September session on preparing students for cyber careers focuses on realism-based training, including how controlled failure can become useful workforce data rather than something educators simply mark wrong. That’s a useful distinction. A score tells you whether someone found the answer. Watching how they reached it tells you much more. A WRONG ANSWER CAN REVEAL THE REAL SKILLS GAP Imagine two SOC trainees investigating the same suspicious activity. Both eventually identify the threat. One gets there systematically. The other clicks through five theories, misses a