The word “bot” doesn’t have the best reputation right now.
You hear it and think of election manipulation, fake social media accounts, scammy customer service chatbots, or malware scanning networks. And for good reason, some estimates say bots now make up over 60% of global internet traffic, and most of that isn’t helpful or harmless.
So I get the skepticism. In an age of deepfakes, data breaches, and AI hype, it’s fair to ask: are there any bots we can trust?
I think so. But only if we define what we mean by “good”—and take seriously what can go wrong even with the best intentions.
Let’s Define the “Good” Bot
In my world, bots aren’t about replacing people. They’re about assisting them. These are narrow, purpose-built automations that operate within clear constraints. They don’t decide what’s relevant in an investigation or make legal calls. What they do is help humans get there faster by organizing, filtering, and surfacing information from huge volumes of messy data.
That’s what I mean by assistive automation. It doesn’t remove people from the loop. It frees them from the repetitive parts so they can focus on what matters.
For example, in a recent insider threat case, an investigator used automation to pull messages, call logs, and chat threads from several mobile devices. What would’ve taken days of manual work happened in a few hours with full audit logs, clear context, and no loss of control. The humans still made the final judgment. The bot just helped them distinguish the signal from the noise faster.
But even that kind of tool, well-scoped and well-governed, can go wrong if we’re not careful.
Why Even “Good” Bots Can Fail
One of the most common mistakes is assuming that a bot, once labeled as “assistive,” is automatically safe. But intent doesn’t prevent failure.
Bots can mislead. They can miss critical context. They can reflect biased training data. And people, especially under time pressure, can over-trust their outputs, a known phenomenon called automation bias.
That’s why it’s not enough to build bots that work. We have to build them to be accountable.
Governance Is the Line Between Help and Harm
But let’s be clear: good intentions aren’t enough. What separates a bot that empowers from one that exploits is governance.
These Bots needs to be designed around four non-negotiables:
- Auditability: Every action the system takes is logged and traceable.
- Explainability: You can see why a result appeared, not just what the result is.
- Data control: Sensitive data stays within protected environments. Period.
- Human oversight: The final decision always lies with the investigator, not the system.
These aren’t just design preferences. They’re requirements, especially in legal, regulatory, and privacy-sensitive environments. They also need to look to frameworks like NIST’s AI Risk Management standards and ISO/IEC 23894 for guidance. Governance isn’t a checklist—it’s the scaffolding.
Let’s Not Pretend All Bots Are Built This Way
To be clear, most bots today are not governed like this.
The internet is full of bots designed for manipulation, fraud, or simple speed with no regard for accuracy or safety. That’s not a failure of the technology; it’s a failure of priorities. And unfortunately, “good” bots are still the minority. They require expertise, investment, and patience – three things not all organizations are incentivized to value.
That’s why I believe part of defending good bots is calling out the gap between what’s possible and what’s common.
None of this requires magic. Just discipline, clarity, and a commitment to human-first design.
Beyond Investigations – More Quietly Useful Bots
While our work focuses on legal and forensic use cases, bots are doing good elsewhere too. Cybersecurity teams use them to monitor and isolate threats in real time. Accessibility tools rely on bots for voice-to-text and screen readers. Even in disaster response, bots help triage calls or flag misinformation.
These aren’t the bots that dominate headlines. But they’re helping quietly, under human supervision, within defined roles, and with auditable outputs.
So What Can We Do?
If you’re building bots, regulate yourself before someone else has to. Design for transparency, not just efficiency. Make explainability a product feature. Keep the human decision-maker in the loop and mean it.
If you’re using bots, ask harder questions. Where’s the data going? Can you audit the result? Would you feel comfortable defending its output in court?
And if you’re a policymaker or leader, reward systems that get it right, not just fast. Because speed without structure doesn’t scale safely.
Final Thought
Bots reflect the priorities of the people who build them. With the right boundaries and the right intentions, they can do more than save time, they can make systems smarter, fairer, and more responsive.
We don’t need to fear automation. But we do need to govern it. And if we do that well, then yes – some bots can absolutely be good.
About the Author
Kousik Chandrasekaran is Director of AI at Exterro. He plays a crucial role in driving Exterro’s AI-driven product initiatives, with a focus on integrating AI throughout the Exterro Data Risk Management Platform. Over his 8-year tenure with the company, Kousik has established and developed the AI function from scratch & launched at least a capability a year. With an engineering degree from IIT Madras, one of India’s premier institutions, he possesses strong technological fundamentals and excels at transforming complex concepts into user-friendly features and capabilities.
Kousik can be reached online at LinkedIn and at our company website https://www.exterro.com/.
