Security | Threat Detection | Cyberattacks | DevSecOps | Compliance

Top tips is a weekly column where we highlight what’s trending in the tech world and list practical ways to explore these trends. This week, we are tackling a lesser-known but growing cybersecurity risk in modern workplaces: printer-based attacks. Let's start with a simple scenario. It's a quiet evening at the office. Most employees have gone home, the lights are dimmed, and the network continues running as usual. In one corner of the floor sits a printer that has been there for years.

Zero-day exploits rarely announce themselves. There is no public advisory yet. No CVE identifier. No detection signature sitting inside a rule library. The vulnerability exists quietly until someone discovers it and unfortunately attackers often discover it first. Once that happens, the exploit becomes a test of visibility. Attackers do not usually rush into environments using zero-days. They explore carefully. They check which systems respond. They observe how security tools behave.

The Terminator is often people’s reference point for artificial intelligence (AI), especially when they worry that technology will be the end of civilization. However, on the other end of the AI spectrum is the beloved, marshmallow fluff Baymax, the helper robot providing assistance to those in his presence. The reality of AI sits somewhere between these two extremes. For security teams, AI initially seemed like a revolutionary technology that would offer faster detection and automated analysis.

AI or artificial intelligence has significantly altered how we work. From customer support bots to internal copilots, they help teams move faster and smarter. But there is a growing concern that many companies are still not ready for. It is data leakage in AI. When an AI agent accidentally or unknowingly shares private information with the wrong person or another system, it is called a data leak. When AI systems handle sensitive data, even a small mistake can expose private information.

AI adoption has accelerated faster than most organizations’ ability to manage it. Security and compliance teams are now responsible for overseeing machine learning models, large language models (LLMs), agentic AI systems, and shadow AI—often with frameworks and processes that weren’t built for any of it. The gap between deploying AI and governing it responsibly is where risk lives. AI governance tools exist to close that gap.