Security | Threat Detection | Cyberattacks | DevSecOps | Compliance

If your organization uses a private large language model (LLM), then it’s time to start thinking about countermeasures for jailbreaking. A jailbroken LLM can lead to leaked information, compromised devices, or even a large-scale data breach. Even more troubling: Jailbreaking LLMs is often as simple as feeding them a series of clever prompts. If your customers can access your LLM, your potential risk is even higher.

Generative AI is rapidly transforming the way we work. The large language models (LLMs) that power tools like ChatGPT and Claude are immensely powerful, capable of providing us with research data, detailed insights, and even deep analysis of documents and data sets, all performed through simple, text-based prompts. However, these prompts have unfortunate side effects for the IT professionals assigned to protect sensitive and proprietary data from cyber attacks.

The history of computing is marked by sea change moments; those times when the world seems to shift into a new possibility space almost overnight. ENIAC. The personal computer. The World Wide Web. The smartphone. And now, AI. While the term “AI” has been applied to many new (or re-branded) services and products, the underlying technology that makes most of them feel like magic is the large language model (LLM).

It’s easy to think about securing an organization’s data like building a bank vault. You focus on defenses that are impermeable to unauthorized parties: doors hardened against drills, walls resistant to impacts, and countermeasures for any number of other illicit access methods. Ultimately, you feel confident that only people with the right clearance will get in.

Modern businesses are more reliant on mobile devices than ever before. Employees need smartphones and tablets for communication, productivity, and even security authentication. As remote and hybrid work setups become more common, mobile technology is necessary for keeping workers connected to their organizations. At the same time, these devices expose a weak link in the cybersecurity chain: the human layer.

The battle against cyber threats is never-ending — and mobile is the new battleground. Modern workers now rely on mobile devices to access sensitive information, often using a single device for personal and professional purposes. As a result, malicious actors have sensed a blind spot and are using these devices as the first line of attack to gain a foothold into secure systems. 1.2 million enterprise employees were exposed to mobile phishing attacks in Q2 2025.

AI adoption is accelerating at an unprecedented rate. A recent McKinsey survey found nearly 80% of enterprises now regularly use generative AI, outpacing the early adoption of both the personal computer and the public internet. Agentic AI—autonomous agents capable of planning, reasoning, and acting on a user’s behalf—has likewise moved from pilots to production, with 79% of senior executives reporting adoption.

Misconfigurations in storage and encryption settings can put your mobile apps at risk, but so can social engineering attacks. Mobile devices are powerful productivity tools, enabling your staff to work from almost anywhere. They can also be security risks, sharing sensitive data outside of a tightly controlled office environment. If smartphones and tablets are integral to your organization’s day-to-day workflows, a mobile application security assessment should be part of your cybersecurity strategy.

Penetration testing is one of the most effective ways to gauge your organization’s cybersecurity readiness. While traditional security tools can block everyday threats, a penetration test (or pen test) demonstrates what might happen if a particularly clever or dedicated threat actor decided to attack your network. A well-executed pen test can reveal unexpected cybersecurity holes in both the technological and human layers at your organization.

Generative artificial intelligence (GenAI) went mainstream in 2022 with the launch of ChatGPT. Now, tech companies are turning their attention toward the next big advancement: agentic AI. Within the next few years, generative AI and agentic AI may coexist in the professional world, synthesizing information and streamlining operations more efficiently than humans can.