Combining Trust Score and Email Authentication in a Risk Formula

Security teams across Australia are quietly shifting away from binary allow-or-block decisions. With the ACSC reporting thousands of phishing incidents every year, organisations from Brisbane to Perth now want a sliding scale of risk that reflects both how a sender behaves and how well their domain is configured.

A custom risk scoring formula lets you combine the platform's trust score with authentication results into a single, tunable metric. Instead of two separate signals sitting in different dashboards, you get one number your team can route, alert on, or feed into a downstream system.

Decoding the Trust Score Signals

Before any formula makes sense, you need to know what goes into a trust score. The platform aggregates domain age, sending history, blacklist presence, and reputation indicators across billions of messages. If you are new to the metric, the trusted sender score FAQ walks through how each component is weighted and why a brand-new domain might score lower than a decade-old one.

The score updates continuously, which matters when you layer it into a formula. A sender that was clean yesterday could spike today if their infrastructure starts routing through a known bulletproof host, and your formula needs to react in near real time.

Pulling Authentication Data into the Mix

Email authentication is its own set of signals. SPF confirms which IPs are allowed to send for a domain, DKIM proves the message was not tampered with in transit, and DMARC ties it all together with a policy that tells receivers what to do with failures. Treat each one as a variable.

The cleanest approach is to score each record independently rather than just checking pass or fail. A DKIM signature with a weak 512-bit key is worse than a 2048-bit key. A DMARC record sitting at p=none with no reporting endpoint does nothing to prevent spoofing. If you want a structured walk-through, the SaaS authentication monitoring guide shows how to collect these results across a portfolio of tenants.

Designing the Weighted Formula

Start by assigning weights that match your threat model. A typical starting point is 40 percent trust score, 25 percent SPF alignment, 20 percent DKIM strength, and 15 percent DMARC policy enforcement. These numbers are not sacred, so treat them as security factors you adjust over time.

A simple expression looks like:

Risk = (TrustScore × 0.4) + (SPF × 0.25) + (DKIM × 0.2) + (DMARC × 0.15)

Multiply the result by 100 to get a 0–100 risk index, then bucket it: under 20 is low, 20–60 is medium, above 60 is high. Document every assumption so a future analyst in your Melbourne SOC can defend the choices when APRA or an internal auditor asks.

Implementing and Automating the Pipeline

Manual scoring dies at scale. Wire your formula into the platform's API so every check on every domain produces a fresh score. Store the historical values so you can chart drift over time, because a sender slowly sliding from 85 to 55 is a much louder signal than a single bad day.

Schedule batch jobs for bulk domain portfolios and trigger real-time re-scores when a domain's authentication record changes. If your outbound mail service adds a new sending IP, the formula should re-run before that IP carries your first campaign.

Tuning and Calibrating Against Reality

A formula that looks elegant on paper can still misfire. Run your scores against a sample of known-bad and known-good senders drawn from real incidents, including reports that crossed the desk of the Office of the Australian Information Commissioner during the latest notifiable data breach period. Look for senders that scored low but were actually benign, and those that scored high but slipped through anyway.

The outbound email risk checklist is a useful starting point for this calibration phase, particularly the sections on false-positive thresholds and escalation paths.

Putting the Score to Work in Daily Operations

Once tuned, route the score into the systems your team already uses. Feed high-risk results into your SIEM, medium-risk ones into a queue for analyst review, and low-risk ones straight to allow log automation. Local teams often pair this with periodic reviews against the ACSC Essential Eight maturity assessments, since both frameworks speak the same language of layered controls.

A custom risk scoring formula is not a one-off project. It is a living model that should be revisited every quarter, especially as authentication standards evolve and adversaries find new ways to abuse trusted infrastructure.

Source	Signal Type	Update Frequency	Best For
Trusted Sender Score	Reputation, history, blacklist	Continuous	Sender behaviour analysis
SPF Check	IP authorisation	Per DNS record	Verifying permitted senders
DKIM Validator	Signature integrity	Per message	Detecting tampering
DMARC Inspector	Policy alignment	Per DNS record	Anti-spoofing enforcement
Combined Formula	Weighted risk index	Configurable	Operational decision-making