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Chief Information Security Officer

Emerging Threat & Technology Evaluation

Enhances✓ Available Now

What You Do Today

Stay ahead of the threat landscape — evaluating new attack vectors (AI-generated phishing, deepfakes, supply chain attacks), emerging security technologies, and how business technology changes affect the attack surface.

AI That Applies

AI-curated threat intelligence feeds that filter the signal from the noise, prioritized by relevance to your specific technology stack and industry. Automated evaluation of emerging security tools.

Technologies

How It Works

The system monitors network traffic, access logs, and threat intelligence feeds in real time. NLP models process the text input by identifying entities, classifying intent, and extracting the structured information needed for downstream decisions. The results integrate into the practitioner's existing workflow — presenting recommendations, flags, or automated outputs alongside their normal working context. The strategic foresight.

What Changes

Threat intelligence becomes actionable. Instead of reading 50 threat reports, the AI surfaces the 3 that actually affect your environment and recommends specific defensive actions.

What Stays

The strategic foresight. Predicting how the threat landscape will evolve and positioning the security program ahead of the curve requires experience, industry connections, and strategic thinking.

What To Do Next

This section won't tell you what your numbers should be. It will show you how to find them yourself. Every instruction below produces a real, verifiable result in your organization. No benchmarks, no projections — just the steps to build your own evidence.

1

Establish Your Baseline

Know where you are before you move

Before adopting AI tools for emerging threat & technology evaluation, understand your current state.

Map your current process: Document how emerging threat & technology evaluation works today — who does what, how long it takes, where the bottlenecks are. You need this baseline to measure improvement.
Identify the judgment points: The strategic foresight. These are the boundaries AI won't cross.
Assess your data readiness: AI tools for this area need data to work. Check whether your organization has the historical data, integrations, and data quality to support Threat Intelligence tools.

Without a baseline, you can't measure whether AI actually improved anything. You'll adopt tools without knowing if they're working.

2

Define Your Measures

What to track and how to calculate it

Time per cycle

How to calculate

Measure how long emerging threat & technology evaluation takes end-to-end today, then after AI adoption.

Why it matters

The most visible improvement is speed. If AI doesn't save time, question whether it's adding value.

Quality of output

How to calculate

Track error rates, rework frequency, or stakeholder satisfaction scores before and after.

Why it matters

Speed without quality is just faster mistakes. Measure both.

When to check: Check after 30 days of consistent use, then quarterly.
The commitment: Give new tools at least 30 days before judging. The first week is always awkward.
What NOT to measure: Don't measure AI adoption rate as a KPI. Adoption follows value — if the tool helps, people use it.
3

Start These Conversations

Who to talk to and what to ask

your board chair or lead independent director

Who on the team has the most experience with emerging threat & technology evaluation — and have they seen AI tools that could help?

They shape expectations for how AI appears in governance

your CTO or CIO

How would we know if AI actually improved emerging threat & technology evaluation — what would we measure before and after?

They own the technology infrastructure that enables AI adoption

4

Check Your Prerequisites

Confirm readiness before you invest

Check items as you confirm them.