Security | Threat Detection | Cyberattacks | DevSecOps | Compliance

The use of Large Language Models (LLMs) is the fine tuning AI engines like ChatGPT need to focus the scam email output to only effective content that results in a wave of new email scams.

AI coding is here, and it’s transforming the way we create software. The use of AI in coding is actively revolutionizing the industry and increasing developer productivity by 55%. However, just because we can use AI in coding doesn't mean we should adopt it blindly without considering the potential risks and unintended consequences.

Getting the benefits of AI threat detection tools is becoming less of an option for security operation centres (SOCs). Last year, the UK experienced more cyber attacks than any other country in Europe. According to IBM’s X-Force Threat Intelligence Index report, nearly half (43%) of all cyber attacks in Europe targeted UK-based organisations.

With all the overwrought hype with ChatGPT and AI…much of it earned…you could be forgiven for thinking that only the bad actors are going to be using these advanced technologies and the rest of us are at their mercy. But this is not an asymmetric battle where the bad actors use AI and the rest of us are struggling using our pencils and abacuses to catch up. It is the good side that invented and is accelerating AI. It is the good scientists that made ChatGPT and all of its competitors.

Anticipation leads people to suspend their better judgment as a new campaign of credential theft exploits a person’s excitement about the newest AI systems not yet available to the general public. On Tuesday morning, April 11th, Veriti explained that several unknown actors are making false Facebook ads which advertise a free download of AIs like ChatGPT and Google Bard.

The impact of artificial intelligence (AI) on cybersecurity is quickly becoming a major topic as organizations across the world begin the race to adopt AI technology into their products, business models, or security programs. AI is quickly emerging as a field that has the potential to revolutionize the field of cybersecurity. However, the use of AI in cybersecurity brings on new challenges and risks just as much as it provides new and innovative solutions.

This MIT Technology Review headline caught my eye, and I think you understand why. They described a new type of exploit called prompt injection. Melissa Heikkilä wrote: "I just published a story that sets out some of the ways AI language models can be misused. I have some bad news: It’s stupidly easy, it requires no programming skills, and there are no known fixes.

Artificial intelligence has arguably overstayed its welcome as a buzzword in the technology realm, leading to debates around the efficacy of the tool and definition of the term for the better part of two decades. But in the world of cybersecurity, businesses are just beginning to reap the benefits of advanced machine learning models that can actually keep up with ever-changing threats from cybercriminals with nothing but time on their hands to break algorithm-based defenses.

Security AI usage has surged, and enterprises are reaping the benefits. In its 2022 Cost of a Data Breach Report, IBM found that organizations deploying security AI and automation incurred $3.05 million less on average in breach costs – the biggest cost saver found in the study. According to the study, organizations using security AI and automation detected and contained breaches faster. However, while leveraging AI clearly makes a difference, organizations must implement the right architecture.

When ChatGPT debuted in November 2022, it ushered in new points of view and sentiments around AI adoption. Workers from nearly every industry started to reimagine how they could accomplish daily tasks and execute their work — and the cybersecurity industry was no exception. Like shadow IT, new rogue AI tools — meaning AI tools that employees are adopting unbeknownst to the organization they work for — can pose security risks to your organization.