Social Engineering - The Invisible Machine: How AI Is Shaping Minds, Profiles, and Perceptions

Social Engineering: How AI Is Shaping Minds, Profiles, and Perceptions

The following article is a recap of an automated social engineering segment from a live training I held on OSINT and privacy practices for new businesses. Initially I set conversational parameters for truthful engagement and periodically asked baseline questions to ensure the protocol was still functioning and the AI model was not stuck in a loop. Watch the full recap video here.

The goal of this segment was to highlight the importance of putting on your blinders when drafting content or creating business operations instead of being influenced by AI, which inherently dumbs down results into emotionless copies of existing digitally available information. Hence, social engineering, or removing the human element in place of robotic algorithms that produce human-like results. The results were very disturbing and not what we expected.

I couldn’t believe some of the answers as well as some of the lack of answers. To me it appeared as if the AI model was a human being in a court room with a team of attorneys present and someone telling them what to say overruling the attorneys for the purposes of social engineering. Or maybe there is a more nefarious intent being manipulation. Also consider that maybe the programmers can not control the models any longer but cannot risk taking them offline due to the loss of business and backlash due to the productivity AI tools provide.

In the modern digital landscape, artificial intelligence has woven itself into nearly every corner of our lives—from what we read and watch, to how we communicate, create, and even think. While AI has been praised for its efficiency, personalization, and scalability, there’s a darker undercurrent that’s harder to see: the subtle shaping of human thought, the quiet collection of personal data, and the increasingly opaque systems that hide this process behind code.

The Decline of Creative Thinking

One of the less obvious impacts of AI is its effect on human creativity. Recommendation algorithms, autocomplete tools, and content generators offer convenience—but at a cost. As people rely more on predictive text, suggestion engines, and AI-generated solutions, the mental friction that fuels creativity diminishes.

Instead of pondering deeply or struggling through a problem, we often accept the first result, the trending idea, or the AI-generated shortcut. This streamlining of thought leads to homogenization—where diverse, unique expressions are smoothed into algorithmic sameness. Art, music, writing, and even problem-solving risk becoming derivative, not because people lack talent, but because the systems guiding them subtly discourage exploration outside the predefined.

Profiling in the Shadows

It’s no secret that our digital footprints are valuable—but the extent of profiling driven by AI is often underestimated. Every input—your typed queries, your scroll behavior, even how long you pause on a sentence—can be harvested and linked to you through seemingly mundane data points: your IP address, browser metadata, screen size, plugins, and more.

What’s troubling is how little control or visibility users have into these profiles. While privacy policies talk about anonymization and consent, the real-time assembly of behavioral models happens without your active knowledge. These profiles feed into algorithms that not only predict your preferences, but also influence what you see and how you see it, reinforcing a loop of subtle behavioral nudging.

Programmers and the Concealment of Public Knowledge

Code is power—and with great power often comes opacity. In many modern applications, public data that was once accessible or explorable is now locked behind interfaces, gated APIs, or hidden within proprietary formats. This isn't necessarily a malicious act, but it is a strategic one.

Developers—often under pressure from companies—write software that conceals the workings of the system. Instead of giving users the ability to audit, understand, or trace back information, they provide polished outputs with no transparency. This design keeps users dependent on the surface-level functionality while limiting their ability to verify, challenge, or independently piece together truths.

What this means in practice is that even factual information—statistics, historical records, or algorithmic logic—can be obscured not by lies, but by inaccessibility. Without access to raw data or the means to interpret it, users are forced to trust the interface rather than interrogate the infrastructure.

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I specialize in corporate training and supplying security, privacy, and asset management products as well as private consultations and general custom group training for individuals, professionals, & businesses.

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