It’s been a while since I last wrote about OSINT, and the landscape has changed a lot. These days, it’s impossible to talk about cybersecurity without bumping into AI. Whether you are scrolling social media, reading the news, or sitting in a boardroom discussion about “digital transformation”, it's everywhere.
And yes, OSINT (Open-Source Intelligence) has caught the AI wave too.
On one hand, AI is making OSINT investigations faster, smarter, and more scalable than ever. On the other hand, attackers are also using AI to supercharge their reconnaissance, phishing, and social engineering campaigns. That’s why I call it a double-edged sword.
Let’s unpack both sides, shall we?
The Bright Side: AI Helping Defenders
For security teams and researchers, AI is like having an extra analyst who doesn’t need sleep. Here’s how it helps:
Noise reduction
- One of the biggest headaches in OSINT is the sheer volume of data. A single breach dump can contain millions of records, and only a small fraction of them may be relevant to your company. AI can help by automatically filtering out the noise and surfacing only the high-value data like leaked credentials tied to your corporate domain or employee emails. This means analysts spend less time buried in irrelevant information and more time acting on real threats.
- Threat actors talk a lot. Whether it’s on underground forums, Telegram groups, or paste sites, there’s always chatter about new exploits, vulnerabilities, or stolen data. The challenge is keeping track of it across multiple languages and platforms. AI, through Natural Language Processing (NLP), can scan this content in real time and flag mentions of specific technologies, IP addresses, or brand names. For example, if someone posts instructions on exploiting a vulnerability in the exact firewall your company uses, AI can pick that up before it’s weaponized against you.
- Not all threats come in text form. Attackers create fake profiles with stolen headshots, run scams with cloned company logos, or spread misinformation through manipulated videos. AI-powered image and video recognition tools can spot these threats by detecting deepfake patterns, matching logos against your brand assets, or identifying stolen images being reused in fraudulent accounts. For instance, some companies already use AI to find and take down fake job ads that use their branding to trick job seekers into giving up personal information.
- OSINT traditionally requires a lot of manual work such as searching Shodan for exposed devices, scanning GitHub for leaked API keys, or digging through social media for impersonation accounts. With AI, this entire process accelerates dramatically. Instead of an analyst spending hours running queries and cross-referencing results, AI can crawl, collect, and categorize findings in minutes. This speed doesn’t just save time. It also makes it possible to respond to threats while they’re still fresh, before attackers can exploit them fully.
Real-World Case Study: AI vs Phishing Emails
The Dark Side: AI in the Hands of Attackers
Of course, the same AI tools that make defenders stronger also give attackers a serious upgrade. Cybercriminals no longer need to spend hours crafting emails or manually running reconnaissance since they can let AI do the heavy lifting. Automated reconnaissance powered by AI can scrape vast amounts of data from the web, social media, and corporate sites in minutes, giving attackers a detailed map of their target’s digital footprint.
When it comes to phishing, AI has practically killed off the old stereotype of clumsy grammar and obvious scams. Today’s phishing emails can be polished, context-aware, and personalized, making them much harder for employees to spot. Add deepfake technology into the mix, and the threat becomes even scarier. Voice cloning and video manipulation are cheap and convincing, making scams like CEO fraud or fake video calls dangerously effective.
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| Scam Incident Involved AI-Generated Deepfake on Arup |
The Grey Area: Ethics and Legal Gaps
The rise of AI in OSINT also brings us into tricky ethical territory. OSINT has always lived in a grey zone as technically it relies on publicly available information, but that doesn’t mean every use of it feels ethical. When AI enters the picture, the line blurs even further. An AI tool can scrape vast amounts of personal data in seconds, often without the knowledge or consent of the people involved. Deepfakes and voice cloning raise even bigger concerns, especially when they’re used for fraud or manipulation.
A well-known example is Clearview AI, which built a facial recognition system by scraping over 30 billion images from Facebook and other sites without asking for permission. While the company claimed it was simply using public data, regulators in Europe hit back with multimillion-dollar fines, and countries like Canada outright banned its use. In Southeast Asia, the debate is still evolving. Many countries treat biometric data like facial images and voice prints as sensitive, but legal protections vary widely, and few regulations directly address AI-driven OSINT scraping. This patchwork leaves organizations with tough questions: where do we draw the line between legitimate monitoring and surveillance, who is accountable if AI-driven OSINT crosses into privacy violations, and how do we prevent these tools from being misused while still benefiting from their defensive potential?
Counter-OSINT: Fighting Fire with Fire
The good news is that defenders aren’t powerless as AI can also be turned against the very techniques attackers use. Counter-OSINT is about using the same intelligence methods to reduce your organization’s digital footprint before it becomes a liability. With AI, companies can automatically scan the web for exposed assets, leaked credentials, or forgotten cloud services that attackers might exploit. Instead of drowning in raw data, AI can prioritize the most critical risks, giving decision-makers a clear picture of where the biggest gaps are. It can also help enforce digital hygiene by flagging when employees overshare sensitive information online or when company emails appear in a breach. For small and medium businesses, this is especially valuable while they may not have the budget for a large SOC team, AI tools can help them play defense at scale and keep up with threats that evolve faster than human eyes alone can catch.
The Road Ahead: AI vs AI
From where I sit as an OSINT analyst, I see the future turning into a battle of AI versus AI. Attackers are already using AI bots to do the kind of reconnaissance that once took hours of manual digging now it happens in minutes, and often at a scale no human could ever match. They’re combining that with AI-generated phishing content and deepfakes that blur the line between reality and manipulation. On the defensive side, I believe we’ll have no choice but to fight fire with fire. AI is becoming essential for filtering through the noise, detecting anomalies in real time, and turning raw OSINT into actionable risk scores that business leaders can actually use. In my view, the winners in this space will be the teams who adapt the fastest and don’t treat AI as a magic bullet, but as an augmentation to human judgment. By 2030, I wouldn’t be surprised if OSINT reports routinely include something like an AI-OSINT Exposure Score, a metric that translates digital footprints into boardroom language. That’s where I see things heading, and it’s both exciting and a little daunting.
Closing Thoughts
AI has changed the game for OSINT. It makes defenders sharper, but it also gives attackers scarier capabilities. That’s why we can’t afford to ignore it.
For CISOs, IT leaders, and even everyday users, the message is simple: don’t wait until AI-powered OSINT is used against you. Start exploring how it can be used to defend you.
After all, in cybersecurity, the sword always cuts both ways. The real question is whose hand will be on the hilt?
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