If you’ve ever built (or used) an AI assistant that can read a web page, summarize a PDF, or draft an email reply, you’ve already touched the core risk behind prompt injection attacks. Prompt injection attacks are one of the most important security problems in modern AI because they exploit…

In today’s retail industry, AI powers everything from demand forecasting to personalized marketing. While these technologies promise efficiency and new insights, they also introduce critical risks, biased algorithms, privacy breaches, fraud, and compliance issues. Forward-looking retailers prioritize AI risk management in retail as a core practice. By building robust governance…

In 2026, enterprises are deploying generative AI and machine learning across the board, from automated customer support to autonomous analytics. This rapid adoption introduces new risks that traditional cybersecurity tools weren’t built to handle. AI models process sensitive data, and attackers now target those models and data flows directly with…

Governments around the world are racing to adopt AI, from speeding up citizen services to improving public health, but this rush raises serious security and governance challenges. Over 70% of public servants now use AI, yet only 18% say it’s done effectively. This striking gap highlights that public sector AI…

AI models introduce entirely new attack surfaces from poisoned training data to crafted adversarial inputs that traditional IT security doesn’t handle. AI Model Security is the discipline of safeguarding machine learning systems and data throughout the ML lifecycle, training, development, deployment, and runtime. It ensures your models behave as intended,…

Artificial intelligence is transforming insurance, speeding up underwriting, claims handling, and customer service. A recent survey found 55% of insurers are already in early or full adoption of generative AI. But with great power comes risk. Insurers that skip solid AI governance may face biased decisions, data breaches, or regulatory…

If you work in a bank, insurer, payments company, or fintech, you’ve probably had a week like this: the fraud team is worried about deepfakes, the risk team is worried about opaque models, security is worried about data leakage from GenAI tools, and compliance is asking, “Which regulation applies here,…

Artificial intelligence (AI) and machine‑learning models are no longer peripheral experiments in banking; they underpin credit underwriting, fraud detection, operational decision‑making, and customer engagement. Survey research from Temenos and the Economist Intelligence Unit shows that over three‑quarters of banking executives (77%) believe that successfully using AI will differentiate winners from…