Structured Trust Assurance

Most people think "security" lives in firewalls, audits, and smart custody setups. Especially in the digital-asset world, we like to point at infrastructure and tools and say: this is where safety comes from. But underneath all of that there is a simpler, sharper point where things become real: the moment someone (or something) signs.

That is the moment intention turns into an irreversible action: funds move, roles change, contracts upgrade, governance shifts. Everything before that is planning. Everything after that is consequences. The main idea of this piece is that security is less about scattered controls and more about how trust is structured around those signing moments. I'll explain how everyday business processes quietly grow into a trust model, how I'm building a tool to make that structure visible, and why this matters for any system where a few actions can change everything.

The Problem: Trust Structures That Grow in the Dark

Organizations don't design a perfect trust model once and then simply follow it. Instead, they evolve it, often without noticing.

A one-time exception becomes the new normal. A temporary bot becomes permanent. A signer group is reused because it's convenient. A single laptop becomes the laptop. Over time, these small choices solidify into a governance structure that nobody explicitly planned, but everybody depends on.

On paper, you might see:

  • neat diagrams of multisigs and roles,

  • policies about "who must approve what,"

  • assurances that separation of duties exists.

In reality, you often find:

  • reused signers across unrelated critical flows,

  • shared devices or vendors on many "independent" paths,

  • shortcuts that only exist in people's heads or in private chats,

  • automation that isn't fully understood or documented.

This gap between how trust is described and how it actually works is where many real risks live. My goal is to close that gap by turning real operations into an explicit, inspectable trust model.

The Tool: A Clear Description of How Work Really Happens

I'm building a tool that takes your actual business processes – the way work really gets done – and turns them into a structured picture of trust. It does this in a way that's meant to be understandable, not mystical.

You start by describing your processes as chains of objects interacting. For example:

a signer uses a hardware wallet → the wallet connects to a laptop → the laptop uses a web UI → the UI sends a transaction to an RPC node → the node broadcasts it → the contract executes a change.

This simple arrow form is enough to capture the core reality: who touched what, through which path, to produce which outcome.

On top of that, you add context that businesses already think about:

  • what value is at risk,

  • how often a process runs,

  • how urgent it usually is,

  • whether automation is allowed or required,

  • what constraints cannot be ignored (jurisdiction, staffing, uptime, regulatory commitments).

Finally, you describe basic facts about the objects involved:

  • who owns or operates them,

  • where they live (regions, environments, time zones),

  • whether they are human or machine actors (people, bots, services, MPC parties),

  • which dependencies they share (same vendor, same cloud account, same device type, same team).

The tool doesn't need a perfect inventory to be useful. It needs honest approximations and a willingness to refine over time.

Inside the System: Graphs, Paths, and Risk Layers

Internally, the tool turns everything you describe into a graph:

  • nodes are objects (people, devices, apps, infrastructure, contracts),

  • edges are actions ("uses," "connects," "signs," "sends transaction," "broadcasts," "executes," "approves," "changes policy").

A business process is simply a path through that graph.

Once this map exists, you can ask direct questions that are hard to answer with documents, spreadsheets, or tribal knowledge alone:

  • Which exact paths can move funds?

  • Which paths can change who controls funds?

  • Which people, devices, vendors, or systems sit in the middle of multiple critical actions?

  • What happens if one object (one laptop, one RPC provider, one deployment pipeline) fails or is compromised?

To make this useful, the tool looks at risk in layers instead of mixing everything together. At a high level, it considers:

  1. Who or what could cause harm? External attackers, insiders, third parties, accidents.

  2. How would that harm happen? Technical compromise, social engineering, physical access, process breakdown, policy or logic misuse.

  3. What makes it easier than it should be? Single points of failure, weak separation of duties, shared dependencies, poor recovery design, missing monitoring.

The point is not to produce an endless list of worst-case scenarios. The point is to attach realistic, concrete situations to the exact objects and paths you already use, and to see how the organization would absorb – or fail to absorb – those situations.

One important internal step is to look at intersections between processes. This is where many comfortable assumptions fall apart. You can have strong controls within each individual process and still end up with a fragile whole if you reuse the same small group of signers or systems across actions that can dominate each other.

For example:

  • the same quorum can approve both contract upgrades and treasury transfers,

  • an upgrade path can silently grant new transfer abilities,

  • one CI pipeline or admin key can indirectly change many "independent" systems.

In such cases, you may have unintentionally created a universal takeover path. Not because anyone explicitly designed it that way, but because of how separate processes compose. This is exactly the sort of pattern a graph reveals clearly.

Outputs: From Map to Change

The tool is built to return a small number of practical outputs. These are meant to help you act, not just think.

1. A Minimal Signer Pool and Hierarchy

First, it proposes a minimal signer pool and signer hierarchy across all submitted processes.

Optimizing for one process at a time is easy but misleading. Real trust lives in the overall picture. The goal is to:

  • keep operations workable (no need for seven approvals across three time zones for simple tasks),

  • avoid dangerous overlaps between critical actions,

  • clearly separate control-plane powers (upgrades, role administration, configuration) from funds-plane powers (transfers, withdrawals).

You still get reuse where it's safe and convenient, but the tool highlights when reuse quietly becomes concentrated power.

2. A "To-Be" Graph: Same Business, Safer Structure

Second, it outputs a modified "to-be" graph: a redesigned version of your system with the same business outcomes but a better trust structure.

This might include changes like:

  • moving from single signers to threshold signing,

  • splitting duties into propose / approve / execute stages,

  • inserting policy gates between the user interface and the RPC layer,

  • separating signer devices, vendors, or teams across critical paths.

You can view the new graph and see exactly what changed compared to the original, and why.

3. Concrete Policy Rules and Guardrails

Third, it generates policy rules: the enforceable guardrails that make the system resilient without depending on perfect behavior.

Examples include:

  • transaction limits and allowlists,

  • timelocks for high-impact actions,

  • step-up approvals when risk is higher (by value, asset type, or destination),

  • rules that define which transaction types are even allowed from a given process path.

If you have ever watched a sound process break because of a single "unusual urgent case," you understand why turning rules into machine-enforced guardrails matters.

4. Organizational and Administrative Controls

Fourth, it proposes organizational and administrative controls.

Not all risk lives in keys and code. Some of it lives in reporting lines, locations, travel patterns, and incentives. For example, the tool may surface needs like:

  • certain signers should not share a direct reporting line,

  • some groups should maintain geographic or time-zone separation,

  • specific people should avoid all being present in the same place for critical periods,

  • clear rules around conflicts of interest.

In these situations, the answer is not "add another key" but "adjust how people are arranged around that key." Technology and governance have to work together.

The Real Benefit: Seeing Trade-Offs Clearly

The most important benefit is not that the tool says "add more security." It is that it gives you clarity.

You get a single map of how authority and value move through your organization. You see how choices made for convenience – signer reuse, extra automation, consolidating on one vendor, shortcut approvals – reshape the trust model over time, whether you acknowledge them or not.

You can also see trade-offs in a grounded way:

  • Sometimes you genuinely need speed.

  • Sometimes you need automation.

  • Sometimes you need a smaller signer set.

The tool is not there to shame these decisions. It shows what they cost in trust, and how you can compensate: stronger separation at the control plane, better guardrails at high-risk points, clearer organizational rules where technology cannot help.

For audits, incident reviews, or internal governance debates, this brings a kind of calm. Instead of arguing from intuition or fear, you can refer to structure:

  • here are the actual paths,

  • here are the overlaps and shared dependencies,

  • here is how actions can dominate one another,

  • here is the redesigned hierarchy,

  • here is the remaining risk we accept on purpose.

Why Now

I'm building this because the ecosystem is maturing in a way that exposes weak trust models:

  • more value under management,

  • more automation and bots,

  • more third-party integrations,

  • more regulation and expectations,

  • more capable and motivated attackers.

The earlier phase – "a few smart people with hardware wallets" – doesn't scale. It slowly piles up implicit trust, and then, suddenly, that implicit structure breaks in ways nobody fully understands.

DLT systems are unforgiving. They don't care how confident someone sounded in a meeting. If you sign the wrong thing, it executes. That's precisely why they're a good environment to learn in: they make it obvious that "security" is mostly about governance, and governance is mostly about how operations are designed.

If I can get this tool right, the outcome won't just be a cleaner multisig configuration or a nicer diagram. It will be a reusable way to translate business decisions into clear trust structures – and over time, to distill a baseline set of rules that hold whether you're running a stablecoin, a protocol treasury, a fintech approval chain, or any other system where a small number of actions can change everything.

That, in simple terms, is STACKED – Structured Trust Assurance in Crypto Key Environment Deployments: not a magic fix, but a way to see how your trust really works, and a way to shape it deliberately instead of by accident.

Structured Trust Assurance - Roman Semko