CTI League

The Rise of the

Ever wonder how a bunch of hackers actually saved lives when the world stopped in 2020? When hospitals started getting hit by ransomware during the first wave of covid, a few guys decided they'd had enough of the "wait and see" approach from big gov.

The was basically born out of necessity on March 14, 2020, founded by Ohad Zaidenberg, Nate Warfield, and Marc Rogers. It wasn't some corporate board meeting project—it was a frantic effort to protect the medical sector and life-saving organizations (MS-LSO) from getting crushed by cyber-attacks.

• Global Scale: They grew to over 1,500 volunteers from 76 countries, spanning all time zones to keep eyes on glass 24/7. (Third Wave Volunteers: Home)
• Pro-Bono Mission: According to Wikipedia, the group focused on neutralizing threats for hospitals and supporting law enforcement without charging a dime.
• The Trust-Based Model: Since they had to move fast, they used a "trust-based" vetting system. New recruits had to be vouched for by existing members or prove their industry standing through a strict code of conduct. It wasn't about background checks—it was about community reputation.

Honestly, seeing this kind of cross-border collaboration is pretty rare. They even got a WIRED25 award for actually making things better in a crisis. But how did they manage that many people on a slack channel? That’s where things get interesting.

Managing the Chaos on Slack

To keep 1,500 hackers from stepping on each others toes, the league set up a massive slack workspace with a very specific hierarchy. They had "vetted" channels for sensitive intel and "triage" channels where raw data came in. They used custom bot-integrations to pull alerts from Shodan directly into slack, so volunteers could claim a "ticket" just by reacting with an emoji. This kept things organized without a middle manager breathing down their necks. It was basically a global, crowdsourced SOC (Security Operations Center) that ran on coffee and emojis.

Operationalizing Threat Intelligence in Enterprise Software

So, how do you actually turn a firehose of raw data into something a CISO can use? The league didn't just sit around—they built a real-time defense engine by plugging straight into the tools we already use in the enterprise.

Moving from reactive "clean up the mess" security to proactive defense is hard. But it’s the only way to protect things like hospitals or retail supply chains. Here is how they did it:

• Visibility stack: They leveraged Shodan to find exposed medical devices before hackers did. Honestly, seeing how many unpatched systems are just sitting on the public web is terrifying.
• Noise reduction: Using Greynoise helped volunteers ignore the "background noise" of the internet so they could focus on actual targeted threats.
• Automated enrichment: By hitting the VirusTotal api, they could instantly flag malicious files across the globe without needing a human to click "scan" every time.

In a retail setting, this might look like blocking a botnet before it hits your checkout api. For healthcare, it's about closing a port on a ventilator. It’s all about that scim/saml life too. Basically, saml (Security Assertion Markup Language) lets these volunteers log into different threat platforms with one password, while scim (System for Cross-domain Identity Management) automatically creates or deletes their accounts as they join or leave the league. It’s the only way to handle access at that scale without going crazy.

Identity Governance in the Age of AI Agents

So, how do we handle it when the "user" isn't even a person anymore? The CTI League had to manage 1,500 volunteers, but at least those were humans you could talk to on slack. Now, we're seeing ai agents—autonomous bots—performing tasks like threat hunting or automated patching. These things need identities too.

This is where the parallel gets real. Just like the league realized they couldn't manually watch 1,500 people, enterprises are realizing they can't manually watch 10,000 bots. As the volume of actors—human or bot—scales beyond what a person can track, automated identity governance becomes the only way to survive. You move from the league's "trust-based" model to a "zero-trust" automated model because bots don't have reputations to protect.

• Identity attribution: Every action an ai agent takes needs to be tied back to a "human in the loop." The league used their vetting process for this; enterprises need strict technical controls for agents.
• scim and saml for agents: We’re seeing a shift where okta or azure entra (now microsoft entra) manages bot identities. But scim wasn't really built for the "intelligence" part of an ai.
• Dynamic permissions: Unlike a human who keeps the same role for months, an agent might need admin rights for five minutes to fix a server, then zero rights afterward.

Honestly, seeing how complex this gets in finance or healthcare is wild. If an agent in a hospital environment misinterprets a threat and shuts down a network, you need to know exactly which api key was used and why the governance layer allowed it.

When the System Fails: The Cost of Bad Identity

What happens when these automated systems actually fail? We saw a glimpse of this when automated "threat blocking" scripts accidentally blacklisted legitimate hospital traffic during the pandemic. If an ai agent has too much power and no identity governance, it can cause a self-inflicted DoS (Denial of Service) attack. Without a clear audit trail—knowing exactly who or what authorized an action—recovery takes hours instead of minutes. In healthcare, those hours are everything.

Lessons for the Modern CISO

So, what do we actually take away from a bunch of volunteers outperforming nation-states during a pandemic? Honestly, the CTI League proved that managing a massive, decentralized workforce is possible, but only if you have the right identity framework.

Building a secure enterprise isn't just about buying the latest okta or entra license. It's about these core pillars:

• Disinformation Resilience: As the league noted in their goals, protecting the "truth" in your data is just as vital as patching a server. In retail, this means stopping fake inventory spikes caused by bots.
• Law Enforcement Sync: Shared intelligence helps public safety. If you're in finance, your threat data shouldn't just sit in a silo; it needs to help the broader ecosystem.
• Agent Lifecycle: The future is managing the identity of every ai agent. You need an audit trail for every automated action, just like the league tracked volunteer work through slack.

At the end of the day, whether you're managing 1,500 humans or 10,000 ai agents, it’s all about that visibility. Stay safe out there.