AegisIntel Advisory · Exposure Engine · Nigeria
The AegisIntel Exposure Engine is a cyber loss quantification framework built for CBN-regulated institutions that need more than a risk rating. It models your threat environment, runs scenario simulations, and produces outputs your BRMC, audit committee, and external examiners can interrogate.
Most Nigerian financial institutions know they have cyber risk. Few can say what it costs. The Exposure Engine converts your control environment and threat profile into quantified loss scenarios — denominated in Naira, mapped to business lines, and structured for board and risk committee consumption. No black box. No vendor lock-in. One accountable advisor who built it and runs it with you.
We define the scope — critical systems, data assets, business lines — and map the threat actors most relevant to your institution's profile. CBN-regulated institutions have a distinct threat landscape; we start there, not with a generic framework.
We assess your existing controls against the scenarios defined — not to audit compliance, but to calibrate how much your controls actually reduce loss frequency and magnitude. This feeds directly into the simulation inputs.
Using FAIR methodology and PERT distributions, we run 10,000 loss simulations across your defined scenarios. Outputs are Naira-denominated and FX-adjusted where relevant. You see a loss exceedance curve, not a single number that hides uncertainty.
We produce a structured report calibrated for three audiences: the BRMC or board (narrative and loss ranges), the audit committee (methodology and assumptions), and management (scenario breakdown and control recommendations). Each version is standalone.
Naira-denominated loss ranges for each defined scenario, with annualised loss expectancy and 90th-percentile exposure figures your finance committee can act on.
A standalone board-level document presenting risk exposure in plain language — designed to satisfy CBN examination questions on cyber risk governance.
A ranked list of control investments by their modelled impact on loss reduction — so your next security budget conversation is grounded in numbers, not instinct.
The underlying Python Monte Carlo model, documented and transferable. You own it. Run updated scenarios as your environment changes without re-engaging from scratch.
A formal methodology document suitable for submission to external auditors, the audit committee, or CBN examiners who ask how cyber risk is being quantified.
A scoped re-run engagement each year to update assumptions, incorporate new threat intelligence, and reflect control changes — keeping your exposure figures current.
Satisfying BRMC and audit committee requirements for quantified cyber risk without building an internal model from scratch.
Demonstrating a credible cyber risk management framework to CBN examiners during licensing, renewal, or regulatory review.
Getting board-quality risk figures without the cost of a Big Four engagement — the model scales to the institution's complexity.
Providing underwriters with a structured, methodology-backed loss exposure statement to support cyber insurance applications or renewals.
FAIR is the only internationally recognised standard for cyber risk quantification. It decomposes risk into frequency and magnitude components, forcing precision about what you actually know versus what you're assuming.
Each scenario runs 10,000 simulated loss events using PERT distributions calibrated to your institution's environment. The output is a loss distribution — not a single point estimate — giving your board an honest picture of the range of outcomes.
Whether you're preparing for a board presentation, a CBN examination, or simply want to understand your institution's true cyber exposure — reach out directly. Engagements are scoped individually; no off-the-shelf packages.