What One Year in AI Security and Governance Changed About How I See AI | Code by Night Learning to Program Journey Log<br>What One Year in AI Security and Governance Changed About How I See AI<br>After one year working around AI security and governance, I trust flashy AI demos less and pay more attention to data, permissions, discovery, and the boring systems around AI.<br>June 18, 2026 · 12 min · Shivam Chhuneja<br>Table of ContentsI thought AI security would be more about security<br>The real adoption story is daily-use AI<br>GTM teams are exactly where this whole thing scales<br>Building automations changed my instincts<br>My ML master’s made the hype harder to trust<br>Job replacement claims feel..well..you know how they feel<br>The change is in my judgement not my job title

I have become the annoying person who asks, “Can I run this locally?”<br>This is mainly because after working around AI security and governance for a year, I have started noticing how easily data leaves the systems we understand and enters systems we barely think about.<br>I admit that this is a slightly irritating change in my personality.<br>A year ago, if I found a tool that helped me automate something, summarize something, enrich some data, or speed up a GTM workflow, my first question was usually: does this work?<br>Now I still ask that. Obviously. I still like useful tools. I probably use AI more than I did before, not less.<br>But there is another question I always ask now:<br>Wait, what did I just give this thing access to?<br>Okay, this was dramatic, I usually ask that question before giving it permissions, but that wouldn&rsquo;t make it that dramatic for this blog, so deal with it.<br>Anyways, that question is probably the biggest change.<br>I still work in marketing and GTM engineering. This is not a “I left marketing and became an engineer” article because that is not true. But over the last year, I have been working in AI security and governance, building more automations, using AI heavily in workflows, and finishing my master’s in data science and machine learning.<br>That combination changed how I look at AI.<br>It did not make me anti-AI. It made me less impressed by the shiny part and more interested in the so called boring (not so boring for me personally though) questions around it.<br>Where is AI being used? What data is going into it? Who approved the tool? What can it access? Is anything being stored? Can we prove what happened later?<br>Annoying questions, basically.<br>I thought AI security would be more about security#<br>Before working in this space, I assumed a lot more of AI security would look like security in the obvious sense.<br>Jailbreaks. Prompt injection. Model abuse. Red teaming. Threats. Demos where someone tricks a chatbot into doing something it should not do.<br>All of that exists and it matters.<br>But one thing I did not expect was how much of the work starts before that.<br>A lot of the problem is just discovering where AI is being used inside an organization.<br>Which teams are using ChatGPT? Which browser extensions did someone install? Which SaaS tools added AI features without anyone really noticing? Which copilots are approved? Which ones are not? Which internal workflows are sending company data to an LLM? Which AI tools are connected to Slack, Google Drive, GitHub, Salesforce, HubSpot, Notion, or whatever else the company lives inside?<br>Before you secure AI, you have to find it.<br>That sounds obvious now but I do not think I fully understood it earlier. I had the more dramatic version of AI security in my head.<br>In practice, the model is often only one piece of the mess.<br>The inventory work makes a difference because companies cannot govern or secure something that they don&rsquo;t even know is around them. If employees are using AI tools through personal accounts, random extensions, shadow SaaS, or features bundled into tools they already use, then the company’s AI policy might be very neatly written and still completely detached from reality.<br>This was one of my first big understandings.<br>AI security is not only about stopping the weird attack.<br>Sometimes it starts with asking, “Where is this even happening?”<br>The real adoption story is daily-use AI#<br>The riskiest AI use does not always look dramatic.<br>Most people are not sitting there thinking, “Today I will violate policy and create a compliance headache.”<br>They are just trying to finish work.<br>A marketer asks an LLM to summarize last week’s campaign data.<br>A sales rep pastes customer notes into a chatbot to write a cleaner follow-up.<br>Someone uploads a CSV to make a quick report.<br>A support person asks for help summarizing tickets.<br>A founder throws meeting notes into a tool because the alternative is reading their own handwriting, which is its own separate threat model.<br>The tasks are small. The intent is normal. The output is useful.<br>That is exactly why it spreads.<br>This is also why the governance problem is hard. If AI only showed up in giant, official, expensive enterprise deployments, it would be much easier to track. There...