For years, the thing that made a spear-phishing campaign dangerous was effort. Researching a target, writing a convincing lure in their context, impersonating a vendor or person they trust, and timing the send all took a skilled human. That effort was the moat. It is gone.
The capability that used to take a nation-state now works for anyone leveraging readily available AI tools. An attacker can point an AI agent at your company, have it read public signals about your people, write a personalized lure for each one, and send from infrastructure that looks legitimate, for about the cost of a cup of coffee. The result is not a clumsy mass email. It is a targeted message that references a real project, a real vendor, and a real person your employees know.
So the test that matters is no longer "can your employees spot a generic phishing email." It is "what happens when an agent as capable as the attacker targets your people on purpose." That is the question the Aegis Red Team Agent answers.
Most phishing awareness tools send templates. They reuse a handful of lures, rotate the subject lines, and report a click rate. Employees learn to recognize the template, the click rate drops, and everyone feels safer.
The attacker is not using the template. The attacker is using an agent that writes a fresh, personalized lure for each target, drawn from live public information. A test built on recognition measures whether your people can spot something they have seen before. It does not measure what happens when they see something they have not.
To know your real exposure, the test has to be as capable as the threat.
The Aegis Red Team Agent is built the way attackers build them, and run the way a partner should. It does four things, and your security team controls every one of them.
The difference from a template tool is not a feature. That is the whole point. You are measuring your exposure to the attack that is actually coming, not to a pattern your people already memorized.
Per-target engagement metrics. Click-through, reply, and credential-submit rates, broken down by employee, team, and lure type. You see who clicked, not just how many.
The lure inventory, with its OSINT trace. Every email the agent generated, paired with the public sources it pulled to write it. This is the part awareness training usually lacks: a concrete example of how a specific person was targeted, and why it worked.
Controls and training recommendations. Concrete next steps, from gateway controls and vendor-impersonation policy to role-targeted training for the people and lure types that performed worst.
An offensive capability earns trust by how it is run, not just by what it can do.
The goal is never to catch employees out. It is to find the gaps an attacker would have found first, and to close them with training and controls.
In one scoped run against a Fortune 500 company, targets, sender infrastructure, and lure tone were tuned with the CISO's team before any send. The agent then ran autonomously and delivered a full report. One in four employees clicked and the security team saw exactly who, which lures worked, and the public information that made each one convincing, before a real attacker had the chance to learn the same thing.
AegisAI comes from the team that built Gmail's defenses and scaled Google Safe Browsing. The same agents that read your email to stop phishing and business email compromise understand how these attacks are constructed. Pointing that understanding at your own organization, with your team in the loop, turns it into a measurement of your real exposure.
Spear-phishing got cheaper and more capable for the attacker. The way to stay ahead of it is to run the same capability against yourself first.
See who clicks before an attacker does. Request a sample report, or book a demo with your security team.
