Instead of relying on annual training or generic reminders, it intervenes right at the moment risky behavior occurs and guides users to correct it. That real-time feedback drives true behavioral change, reducing future risk at its source and eliminating the noise that floods security teams today. Additionally, enterprise policy can be tailored to take additional remediation measures when self-policing does not work. Self-policing is the best way to reduce confusion, chaos and costs related to non-security events.
Self-Policing Built for Human-Centric Defense
Traditional awareness programs try to fix behavior after the fact. They send long videos or quizzes that people click through once and forget. No one’s driving has really improved from online traffic school. TOTAL takes an entirely different approach.
When a user demonstrates unsafe activity, the system engages them directly in the moment. It could be a series of failed Passwordless logins that suggest careless typing or MFA fatigue, an 18-hour active session indicating a workstation left unlocked, or a login to payroll from a personal laptop over a cafe VPN. In each case, TOTAL delivers a short, configurable message through Slack, Teams, or an on-screen prompt that explains what happened, why it matters, and what to do next. The feedback is immediate and precise — a gentle intervention that turns a weak security habit into a moment of correction.
TOTAL can also recognize higher-risk actions, like repeated privilege escalation requests outside a user’s role or attempts to disable endpoint protection. In those cases, the system may require step-up verification or trigger a micro-training module that reinforces least-privilege and safe-access principles. Every engagement is completely configurable and tailored to the behavior, designed to educate without interrupting legitimate work.
In addition, a user’s response to self-policing becomes a signal in itself. Someone who corrects their behavior right away demonstrates awareness and accountability, while repeated risky actions can reveal deeper issues that require attention. TOTAL captures these signals and feeds them into our Predict and Enforce modules, continuously refining how the platform interprets risk, intent, and trust across the workforce.
The Bottom Line
TOTAL Self-Policing drives real behavioral modification at scale. By intervening in the moment risky actions occur and learning from how users respond, it turns everyday behavior into an active defense mechanism. The result is fewer alerts for the SOC and a workforce that continuously reinforces the organization’s security posture through stronger security hygiene.