Using AI in Threat Assessment: 5 Practical Ways to Improve Case Analysis

Let the Robot Organize the Mess, Not Run the Meeting

Threat assessment has an information problem. Not a lack-of-information problem. Usually the opposite.

A team gets a concerning email. Then a faculty member forwards a screenshot. Campus police has an incident report. HR has three months of messages. Someone remembers “another weird thing last semester,” but nobody remembers exactly when. Five people have five pieces of the puzzle, and the puzzle box has apparently been thrown into a lake.

This is where artificial intelligence can actually be useful. AI is very good at doing the work humans tend to hate: sorting, organizing, comparing, finding dates, identifying contradictions, and turning 300 emails into something resembling a timeline instead of a cry for help. The key is knowing where to stop.

AI Is a Great Intern. It Is a Terrible Supreme Court Justice.

One of the most useful ways to think about AI in threat assessment is as a very capable intern. Give it a clear assignment, and it can do impressive work. Ask it to review interview transcripts, pull out dates, identify recurring grievance language, organize events chronologically, or highlight conflicting accounts, and it may save hours of staff time.

But nobody should walk into the assessment meeting and announce, “ChatGPT says this person is high risk, so I guess we’re done here.” That is the dreaded machine verdict.

Threat assessment is not about asking a computer to predict who will become violent. Human behavior is far too messy for that. A concerning statement, weapons interest, social isolation, mental-health concern, or angry post can be meaningful, but none automatically tells us where a person is headed.

The better question is not, “Is this person dangerous?” It is: “What information here deserves a closer look?” That is a much better job for AI.

Practical Takeaway 1: Use AI to Build the Timeline

Threat cases frequently arrive as a digital yard sale: police reports, emails, interviews, screenshots, case notes, text messages, social-media posts, and random dates written as “last Tuesday.”

AI can help transform that mess into a chronology. It can extract dates and events, normalize approximate time references, merge multiple sources, preserve source attribution, flag conflicting accounts, identify gaps, and update the timeline as new information arrives. That matters because behavioral change often makes more sense over time.

One disturbing email may be meaningless. Twelve increasingly angry emails over six weeks, followed by target research and unusual access attempts, tell a different story. Use AI to help see the movie, not just individual frames.

Practical Takeaway 2: Look for Convergence, Not Magic Keywords

A common mistake in threat detection is searching for scary words.

“Gun.”

“Kill.”

“Bomb.”

“Suicide.”

Unfortunately, language is complicated. A student writing an essay about The Bell Jar should not automatically become a suicide-risk case. A photograph containing a weapon does not necessarily mean someone is preparing an attack. Context matters enormously.

AI is more useful when asked to look for convergence. Is grievance becoming more intense? Is fixation narrowing toward a particular person? Is leakage becoming more specific? Has someone moved from generalized anger toward questions about time, place, method, or target? Weak signals become more meaningful when they begin clustering together.

Practical Takeaway 3: Ask AI to Argue With You

One of the smartest uses of AI is not asking: “What proves my theory?” Instead, ask: “What else might this be?”

Have the system identify alternative explanations. Ask what information would disconfirm your current assessment. Ask which conclusions are facts and which are interpretations. Ask what important questions were never asked. This is essentially red teaming your own thinking.

The program calls this extracting, weighing, and challenging information. The challenging part may be the most valuable because threat assessment teams, like every other group of humans, are vulnerable to confirmation bias.

Practical Takeaway 4: Keep Facts and Interpretations Separate

Consider these two statements:

• “Student purchased ammunition.”

• “Student is preparing for violence.”

Those are not the same sentence. The first is an observable event. The second is an interpretation. AI can help teams keep those categories separate, which reduces the risk of assumptions slowly turning into “facts” simply because they have been repeated in three meetings and a case note.

Practical Takeaway 5: More Alerts Do Not Automatically Mean More Safety

False negatives are frightening because a system may miss something important. But false positives have consequences too. They can lead to unnecessary investigations, police involvement, stigma, discipline, damaged trust, and disproportionate scrutiny of certain communities. Too many false alarms also create alert fatigue. Eventually, everyone starts treating the warning system like the car alarm going off in the parking lot for the forty-third time.

The goal should not be more alerts. The goal should be better questions.

Using AI in Threat Assessment: The Bottom Line

AI can help threat assessment teams find information earlier, organize cases faster, identify patterns, build timelines, generate investigative questions, and challenge their own assumptions.

It should not replace interviews, multidisciplinary discussion, contextual judgment, or professional accountability.

A useful four-step model is: Extract → Weigh → Challenge → Decide

AI can help heavily with the first three. Humans still own the fourth. That may ultimately be the best way to think about AI in threat assessment. Do not ask it to become the smartest person in the room. Ask it to help the people in the room become more curious.