CEO insights: holding on to the human line in the age of AI adoption

As part of our recent AI series, I’ve been sharing my insights on the key topics, questions, and debates currently shaping the industry.

I have covered my opinions regarding holding the human layer sacred in the AI era, where I explored what I deem is the beating heart of the Bug Bounty industry, AI strengths and weaknesses, where human hackers fit in, and what businesses will face in the next 3 to 5 years.

I then looked beyond the AI Model card, where I discussed continuous mechanisms for radical transparency, what giving back to the hacking community really means, and the advantage of consistent reasoning.  

Today I want to discuss an element that, quite frankly, keeps me up at night. And that’s the speed at which the market is moving to adopt AI as a substitute, and even a potential replacement, for human talent.

A key challenge with the market direction

If I look back to a mere year ago, customers were initially concerned about AI being used anywhere near sensitive work. Today, AI is widely adopted and embraced. Soon, the conversation is likely to evolve to ‘Why are you not using AI more?’

That shift is already very visible.

The challenge, however, lies in staying principled in a market that will, very shortly, push hard in the direction of AI, regardless of impact, making holding the human-shaped line tougher.

The perception of AI as a cost-reduction tool  

The challenge is that AI is increasingly being perceived as a cost-reduction tool or as a potential replacement for humans.

The issue here is that if enough organizations buy that pitch, the entire ecosystem's economics get pulled in a direction that's worse for hackers and ultimately worse for security outcomes, on a global scale.

The end goal

The market will likely offer endless opportunities to substitute under the banner of empowerment. 

Holding that line is a daily choice, and we expect the community to hold us to it just as hard as we hold ourselves.

Our job is to prove, repeatedly, that the platforms that win on outcomes win on the human layer. We must be willing to make that case even when it costs us a deal.

Where will future talent come from?

The community doesn't replenish itself automatically. So, let me pose the question: If AI takes the entry-level work, where will senior researchers come from in ten years from now?

There needs to be investment in researcher development, mentorship, and pathways into the craft. But that's harder for businesses to justify quarter-to-quarter than shipping the next AI feature.

The tension between speed and depth

Speed and depth pull in opposite directions if you're not careful, and the operational pressure to move fast can quietly erode the space for the patient, specialized, and careful work that produces the most important findings.

Quality over quantity is the rule. It always has been.

At Intigriti, we want to do the right thing; that's what we're known for, and it's what we're proud of.

No AI feature, efficiency target, or customer pressure changes that.

Mechanisms that keep us on the right path

• Demonstrate impact. Part of our job is showing, rather than telling, that a continuous AI-assisted scan is not a substitute for time-boxed human research; they are complementary.

• A protected lane for complex submissions. Some reports need senior researchers and engineers to read carefully. We protect that lane. Quality is what matters.

• The community is the canary. When a hacker submits a vulnerability, the value goes beyond the finding. The dialogue between the hacker, customer, and Intigriti adds context, reveals nuance, and can signal where future risks may emerge.

A combined approach

To summarise, we don't optimize for speed alone. We optimize for outcomes. Speed is one input. Depth is another. Our job is to keep both moving in the same/right direction and to never sacrifice one for the other.

Humans and AI working together can deliver extraordinary results, but only when the right intelligence is applied in the right way. That means combining deterministic, agentic, and human-led methods, and knowing when to use each.

Next steps

Over the last few weeks, we have released lots of content around the questions, debates, and considerations of AI within the industry. Check out our knowledge base to view these.

Or, if you just want the highlights each month, sign up to our newsletter, and connect on socials to stay in the loop on all AI discussions!