MindVault Concept: Human Confidence Signal. The live High, Medium, Low, or Insufficient confidence level for the person operating a session, delivered to the tools a team already uses. For a SOC, it answers the first question in almost every identity alert: is this still our user?

Most SOCs already have playbooks that could contain a hijacked session in seconds:

  • Revoke the token.
  • Isolate the endpoint.
  • Force a password reset.

The automation exists. In many teams, it still waits for a person to click approve.

That caution makes sense.

If a playbook ends an executive's session on a false alarm in the middle of a board presentation, the SOC loses more trust in one afternoon than it gains from a month of fast containment. So analysts keep a hand on the switch. A playbook that could run in seconds runs in thirty minutes.

Why identity triggers are the hardest

Alert volume is real. In a 2023 survey of 2,000 security analysts, commissioned by the vendor Vectra, SOC teams reported an average of 4,484 alerts a day and said they could not get to 67% of them.

Identity alerts add their own problem. Most arrive as environmental anomalies:

  • a new location,
  • a new device,
  • an impossible-travel flag.

In a hybrid workforce, those fire constantly for innocent reasons, as we covered in From Alert Fatigue to Behavioral Signal.

The alert says something about the account is unusual. It does not say whether the account owner is the one at the keyboard. That missing answer is exactly what an analyst looks for before approving containment. It is why the human step exists.

Researchers in human factors have studied this pattern for decades. In 1997, Raja Parasuraman and Victor Riley described how people stop relying on automation that raises false alarms. In 2004, John Lee and Katrina See argued that trust works best when it is calibrated, matching what a system can actually do. Their thinking shaped the four tests below.

What a trustworthy trigger looks like

A signal earns automation by passing four practical tests.

  • It is calibrated. When it says low confidence, that proves right at a rate your team measured in your own environment, not a rate quoted in a brochure.
  • It explains itself. The analyst can see what changed, when, and which evidence moved.
  • It admits gaps. It says "insufficient evidence" when it cannot judge, instead of guessing.
  • It adds context to other alerts. An unusual login with high operator confidence reads very differently from the same login with low confidence.

MindVault's signal is built around those tests, with an explanation of the change behind each confidence level.

Start with reversible actions

Even a good signal should earn its way up the ladder. Automate the reversible steps first:

  • Low confidence plus a sensitive action: ask for a quick verification automatically. The worst case for a real user is a few seconds of friction.
  • Low confidence plus an unusual login: open a high-priority case with the evidence attached, so the analyst starts from an answer instead of a question.
  • Ending a session or isolating a machine: keep it with a person until your pilot data shows the signal has earned more.

That order matches NIST's session monitoring guidance, which lists reauthenticating the user, ending the session, or notifying support staff as responses to possible fraud. It is the same thinking as The Proportionate Response Model, applied to the playbook instead of the user.

Measure it, do not assume it

The useful pilot question is narrow:

Of the identity alerts that reached an analyst last month, how many could have been closed or escalated correctly using operator confidence alone?

That number, measured on your own users, decides whether any playbook step should run without a person.

  • Low number? You learned something cheaply.
  • High number? You have a case for automation that your own analysts helped build.

Either way, the next decision your SOC makes is backed by its own data, not a vendor's promise.

MFA proves who logged in. MindVault measures who stayed.

Frequently asked questions

Can a confidence signal fully automate session containment?

It can be part of the decision, but the decision should follow your policy and the accuracy you measured in your own environment. Most teams should automate reversible steps first and keep hard actions with an analyst until the data supports more.

What does "insufficient" mean?

It means there is not enough evidence to judge, for example on a new device or in an application the sensor does not cover. Treat it as a gap, not as a sign of attack.

Will this reduce analyst workload?

It is designed to answer the first question in most identity investigations, whether this is still the authorized user, before the analyst opens the case. How much that saves in your SOC is something to measure in a pilot.

Sources: Vectra, 2023 State of Threat Detection, survey of 2,000 security analysts conducted by Sapio Research (vectra.ai). NIST SP 800-63B-4, Section 5.3 Session Monitoring (pages.nist.gov). NIST SP 800-61 Revision 3, Incident Response Recommendations (csrc.nist.gov).

Ideas we learned from: Raja Parasuraman and Victor Riley, "Humans and Automation: Use, Misuse, Disuse, Abuse," Human Factors, 1997 (doi.org). John D. Lee and Katrina A. See, "Trust in Automation: Designing for Appropriate Reliance," Human Factors 46(1), 2004 (doi.org).