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East Africa Frontline News

NextFrontier Africa is a media platform spotlighting Africa’s emerging opportunities in technology, tourism, aviation, agriculture, health, logistics, real estate, trade, education, and e-commerce.

September 09.2025

2 Minutes Read

Decoding AI: The Impact of Generative vs. Agentic AI in Cybersecurity

Three men answering tech questions, think series.

Understanding Generative AI vs. Agentic AI: Key Differences

Artificial intelligence (AI) continues to evolve and spark debates regarding its implications and applications. Two prominent facets of AI are generative AI and agentic AI. While often confused, they serve distinct functions. Generative AI focuses on creating content—be it text, images, or music—prompted by user inputs. It's reactive and thrives on generating responses based on given cues.

In 'Ask the Experts: Gen AI, Cybersecurity, & AI Agent Questions Answered', experts delve into the crucial differences between generative and agentic AI, sparking a deeper analysis of their implications and applications.

Agentic AI, however, operates on a more advanced level. It’s goal-oriented, meaning it undertakes complex tasks with minimal human intervention. This type of AI not only plans but also executes steps autonomously and adapts its approach as circumstances change. For instance, agentic AI is pivotal in cybersecurity, automating response protocols without human oversight, and managing multi-step operations effectively. Understanding these differences is crucial for professionals navigating the evolving tech landscape.

The Dark Web: An Inevitable Challenge

The dark web remains a highly debated topic, often misunderstood as merely a hub for illicit activities. Yet, according to cybersecurity experts, blocking the dark web is not only impractical but may not be desirable. Its elusive nature—small in size and unindexed—makes it difficult to police. Additionally, there are aspects of the dark web that do serve positive functions, including protecting free speech in oppressive regimes and aiding research into cyber threats. In essence, embracing the dark web with caution may yield valuable insights instead of outright prohibition.

The Reality Behind AI Hallucinations

AI hallucinations—situations where AI presents confidently incorrect information—are a significant concern. As AI systems are predominately trained to predict the most likely next word rather than to provide factual accuracy, the risk of them generating plausible-sounding but erroneous content persists. This phenomenon particularly affects recent or niche topics lacking adequate training data. Therefore, human oversight remains a necessity in AI development and utilization, underlining the importance of a robust validation framework to mitigate risks associated with AI applications.

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Decoding AI: The Impact of Generative vs. Agentic AI in Cybersecurity

Understanding Generative AI vs. Agentic AI: Key Differences

The Dark Web: An Inevitable Challenge

The Reality Behind AI Hallucinations

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