Security | Threat Detection | Cyberattacks | DevSecOps | Compliance

AI tools have become a workplace staple, but their unsanctioned use has given rise to shadow AI. It refers to the untracked usage of any AI tools or applications without approval or overview of the information technology (IT) or security team. This is substantiated by the Verizon 2026 Data Breach Investigations Report (DBIR) which shows how rapidly the shadow AI trend is growing.

There’s an important metric that can tell you exactly how vulnerable your high-risk employees and departments are to the next generation of social engineering. It’s not phishing click rates or training completion percentages. It probably doesn’t show up on any security dashboard. It’s the precise number of days it takes your team to respond to a live, context-specific threat with adequate training – training time to market.

You can build a masking demo in an afternoon. Run a regex for credit card patterns, swap the match for XXXX, and ship it. The demo works, the compliance slide says “no PII sent to the LLM,” and everyone moves on. That demo is fooling you by leaving things out. It works because the input is a) clean (card 4111 1111 1111 1111), b) because the only sensitive thing in it is a textbook PII pattern, and c) because nobody downstream ever needs to use the value again.

Medtronic Plc is an American-Irish medical device company founded in 1949. As one of the largest medical device companies in the world and with over 90,000 employees, the company operates in about 150 countries. Its products treat 70 health conditions, helping an estimated 75 million people globally every year. Earlier in 2026, the company was the victim of a cybersecurity incident that impacted some of its corporate IT systems.

Here is a number that should stop every CISO cold. Gartner projects that by 2028, 25% of enterprise GenAI applications will face five or more security incidents per year, nearly triple the 9% recorded in 2025. The acceleration is not slowing. Meanwhile, research by OpenText and the Ponemon Institute finds that 79% of organizations have not yet reached full AI maturity in cybersecurity, meaning most enterprises are deploying generative AI without the foundational controls needed to govern it.

If a known exploited vulnerability appeared on your internet-facing application right now, what would your team actually do in the next 12 hours? What would actually happen, given your tooling, your sprint cycle, your change management queue, and who is available. CERT-In’s blueprint sets these timelines because generative AI and autonomous agents have collapsed the attacker timeline to the point where anything longer is already too slow.

AI is becoming part of almost everything: customer support, security operations, software development, research, analytics, internal workflows, and, most importantly, drafting emails. AI is increasingly embedded in real business processes, and that creates new risks, not to mention the level of unprecedented access mainly of these platforms to our data. Token torching (a type of Denial-of-Wallet (DoW) attack) is one emerging AI risk.

Vulnerability assessment is a systematic process that finds, assesses, and prioritises vulnerabilities in a system or application. Vulnerability assessment is considered a part of a larger family of vulnerability management. Vulnerability management is related to vulnerability analysis to identify conditions that lead to decision-relevant outcomes.