Practitioner Profile
Matthew Brian Tahir
Decision Systems Engineer · Forensic Systems Engineering
I am a Decision Systems Engineer working at the intersection of forensic reasoning, systems analysis, and institutional governance.
My work focuses on identifying failure boundaries, classifying institutional drift, and producing structured case files that support defensible decision-making in complex, high-uncertainty environments.
I do not provide recommendations or speculative analysis. I produce forensic findings — structured assessments of whether a decision is built on evidence sufficient to withstand challenge. The output is a decision packet, not an opinion.
I am preparing for the MSc in Complex Systems Engineering, which provides the mathematical and methodological foundations — stability analysis, network theory, risk propagation — that underpin this practice.
Discipline
Forensic Systems Engineering
Primary Protocol
SOP-001 — Validation & Forensic Standards
Evidence Standard
Daubert-aligned
Academic Pathway
MSc Complex Systems Engineering (TU Delft)
Sector Focus
Infrastructure, Capital Markets, AI Governance, Public Institutions
Jurisdictions
UK, EU, SEA
Status
Active — accepting decision-critical engagements
Not a decision-modelling practice
A Forensic Systems Engineering unit
This practice examines how institutional decisions are made, what evidence they rely on, and where the system is structurally vulnerable to drift, failure, or irreversible liability.
Every engagement produces a forensic case file — not an advisory document, not a report, not a recommendation. A chain-of-custody record that can be challenged, audited, and defended.
This is not
Data quality management
Metadata stewardship or DAMA DMBoK
Operational analytics or modelling
Predictive advisory or recommendations
AI development or engineering
This operates at
Systemic exposure mapping
Institutional drift classification
Cross-sector risk propagation analysis
Irreversible liability identification
Forensic defensibility of decisions
Origins of the Practice
This practice emerged from repeated exposure to environments where decisions were being made faster than they could be validated.
Across sectors — infrastructure, capital markets, public institutions, and AI-driven systems — the same structural pattern appeared: decisions were fragile because the evidence behind them was untested, undocumented, or misinterpreted.
Traditional analytics could not solve this. Governance frameworks could not keep pace. Advisory models produced opinions, not defensible records. The gap was not technical — it was forensic.
"The gap was not technical — it was forensic."
Founding observation · VFS Practice
Three recurring systemic failures
Drift without detection
Institutions were making decisions based on assumptions that had quietly shifted. No record existed of when the drift occurred or who had authorised it. The decision looked sound on the surface because no one had the framework to test it.
Evidence without provenance
Data, reports, and automated outputs were entering decision records without a chain-of-custody. They could be cited, but they could not be defended. Under challenge, the evidentiary foundation collapsed.
Decisions without boundaries
High-stakes decisions were being made without identifying the failure boundary — the exact point at which the decision becomes indefensible, irreversible, or structurally unsound. The exposure was invisible until it materialised.
Development of SOP-001
SOP-001 — the Validation & Forensic Standards protocol — was developed to create a repeatable, defensible method for examining decisions under uncertainty.
The protocol was refined through dozens of case files across multiple sectors, each revealing the same structural requirement: an evidence-first method for validating decisions before they enter institutional memory.
SOP-001 Integrates
Systems analysis
Forensic reasoning
Daubert-aligned evidentiary standards
Drift classification
Failure boundary mapping
Chain-of-custody documentation
Why This Work Exists
Modern institutions increasingly operate in conditions where traditional governance is too slow and traditional analytics too narrow. Each condition creates a specific structural risk.
Condition
Structural Risk
Automated outputs
No chain-of-custody
Cross-sector data flows
Propagation without audit
High-velocity decisions
Structure absent under pressure
Opaque AI systems
Evidence gaps undetected
Distributed accountability
Authority unverifiable
Forensic Systems Engineering is not a replacement for governance or analytics. It is the layer that ensures both remain trustworthy — that decisions remain defensible, evidence-based, structurally sound, and free from silent drift.
AI tools can generate outputs with stated confidence intervals — "94% success probability," "low systemic risk" — but they cannot provide the forensic chain-of-custody required to defend those outputs under institutional challenge.
This practice interrogates AI-generated claims using SOP-001 to establish whether the evidence behind an automated output is admissible, whether the failure boundary has been correctly identified, and whether the output can enter a decision record without creating irreversible institutional liability.
AI governance at this level is not about auditing models. It is about ensuring that what the model produces does not silently become what the institution decided.
SOP-001 Interrogates
Drift Risks
Failure Boundaries
Evidence Gaps
Irreversible Liability Pathways
Forensic Case File Development
Full five-field intake, evidence grading, constraint matrix, and band classification. Output is a signed, chain-of-custody decision packet.
Failure Boundary Review
Structured assessment of where a decision or system becomes irreversible. Identifies the exact conditions under which recovery is no longer possible.
AI Governance Interrogation
Forensic cross-examination of AI-generated outputs against SOP-001. Identifies drift risks, evidence gaps, and liability pathways before outputs enter institutional records.
Drift & Exposure Classification
Band A/B/C classification of institutional exposure. Determines which decisions require mandatory human review and which trigger escalation.
Pricing — Scope-based. Determined by decision horizon, complexity, and band classification. No standard rates. Engagements are accepted selectively.
MSc Complex Systems Engineering
The MSc programme at TU Delft provides the mathematical and methodological foundations that underpin forensic systems practice: stability analysis, network theory, control systems, and systemic behaviour modelling.
The academic pathway is not a pivot. It is the formalisation of what the practice already requires — rigorous frameworks for reasoning about complex, interdependent systems under uncertainty.
Academic Area
Practice Application
Stability Analysis
Failure boundary identification
Network Theory
Cross-sector propagation mapping
Control Systems
Drift classification and correction
Risk Propagation
Band B/C exposure modelling
Systemic Behaviour
Institutional decision architecture