Threat Modeling — Because “What Could Go Wrong?” Is Not a Methodology

Ask a few security engineers how to threat-model an application and you will probably get several different answers. One will recommend STRIDE. Another will bring up MITRE ATT&CK. Someone else will insist on PASTA. And, somebody will suggest drawing a data-flow diagram and thinking really hard about it.

The funny thing is that these people may all be right.

The problem is that threat modeling isn't one specific technique. It's a collection of approaches for understanding what we're building, how it might be attacked, what the consequences would be, and what we're going to do about it.

Some approaches help us discover threats. Others help us understand attacker behavior, assess business risk, analyze privacy concerns, or prioritize mitigations. Treating them as interchangeable methodologies creates unnecessary confusion.

Let's look at the major approaches and where each one fits.

First, What Are We Actually Trying to Accomplish?

At its simplest, threat modeling is a structured way to reason about the security of a system before an attacker finds its weaknesses for us.

A useful starting point is to answer four questions:

1. What are we building?
2. What can go wrong?
3. What are we going to do about it?
4. Did we do a good enough job?

Those questions are intentionally broad. They work for an API, an authentication service, a cloud deployment, a payment system, or an entire enterprise architecture.

The challenge is turning those questions into something repeatable enough that different people can perform the analysis and reach useful conclusions.

That is where the different threat-modeling approaches come in.

STRIDE — Because Six Categories Are Better Than Staring at a Blank Page

STRIDE is one of the best-known approaches to identifying threats in software architecture. Originally developed at Microsoft, it provides six categories of threats to consider when examining system components and interactions.

The acronym stands for:

• Spoofing: Can an attacker impersonate a user, service, or other identity?
• Tampering: Can an attacker modify data, messages, configuration, or software without authorization?
• Repudiation: Can an actor deny performing an action because the system lacks adequate evidence?
• Information Disclosure: Can sensitive information be exposed to an unauthorized party?
• Denial of Service: Can an attacker make a service or resource unavailable?
• Elevation of Privilege: Can an attacker gain permissions beyond those intended?

STRIDE is particularly useful when designing applications and APIs. Start with a data-flow diagram showing processes, data stores, external entities, and the flows between them. Then examine the relevant elements and interactions through the six threat categories.

Consider an application that accepts an access token and calls a backend API.

• Spoofing: Can an attacker impersonate another user?
• Tampering: Can a token or request be modified without detection?
• Repudiation: Can the system establish which identity performed a sensitive operation?
• Information Disclosure: Can the API return data belonging to another tenant?
• Denial of Service: Can an attacker exhaust the API's resources?
• Elevation of Privilege: Can a user invoke an operation they aren't authorized to perform?

Notice that the questions are more specific than simply asking whether the API is secure.

STRIDE gives a team a repeatable way to explore threats that might otherwise be overlooked. It does not, by itself, establish which threats are most important, how likely they are, or which mitigations offer the best return on investment.

That distinction matters. Finding threats and deciding what to do about them are different activities.

PASTA — Because Technical Risk Should Connect to Business Risk

PASTA stands for Process for Attack Simulation and Threat Analysis — I suddenly have a craving for spaghetti. It is a risk-centric approach that connects business objectives, technical architecture, attack scenarios, and the potential impact of successful attacks.

Rather than starting and ending with a list of technical threats, PASTA encourages the team to work through a structured process that includes:

1. Defining business objectives.
2. Defining the technical scope.
3. Decomposing the application.
4. Analyzing threats.
5. Identifying vulnerabilities.
6. Modeling and analyzing attacks.
7. Assessing risk and selecting countermeasures.

The precise work within each stage depends on the system and the assessment.

Imagine we're threat-modeling a payment-processing service. A list of possible attacks is useful, but it doesn't tell us which risks matter most to the business.

An outage lasting five minutes, fraudulent transfers, exposure of customer payment information, and compromise of the administrative control plane have very different consequences. PASTA encourages us to evaluate attack scenarios in the context of those business consequences.

The benefit is a stronger connection between security decisions and business priorities.

The trade-off is effort. A full PASTA assessment requires more analysis, information, and coordination than a quick STRIDE review. That investment makes sense for critical systems and high-impact decisions, but may be excessive for a small internal application.

MITRE ATT&CK — Because Knowing the Threat Category Isn't the Same as Understanding the Attack

MITRE ATT&CK is one of the most useful resources to bring into a threat-modeling exercise, but it is important to understand what it is.

ATT&CK is a knowledge base of adversary tactics and techniques derived from observed real-world behavior and other threat intelligence. It organizes information about how attackers pursue objectives and execute their operations.

It is not, by itself, a complete threat-modeling methodology.

STRIDE might tell us to investigate elevation of privilege. ATT&CK helps us reason about the kinds of adversary behaviors that could lead to it.

For example, suppose an application uses a service account to access a database. A STRIDE analysis might identify the risk that an attacker could gain privileges beyond those intended.

An ATT&CK-informed investigation could then ask more specific questions:

• Could stolen credentials be used to access the service account?
• Could the attacker abuse the application's permissions to access other resources?
• Could the attacker move from the compromised application to another system?
• What evidence would be available to detect the activity?

These questions help connect architectural threats to realistic attack behavior, detection opportunities, and defensive controls.

ATT&CK is especially useful when the objective extends beyond secure design into security operations, detection engineering, incident response, or adversary emulation.

It also helps teams use a common vocabulary when describing attacker behavior.

The important distinction is that STRIDE helps identify classes of threats, while ATT&CK helps characterize adversary behavior. Using them together is often more valuable than choosing one over the other.

NIST SP 800–154 — Because Sometimes the Data Is the Real Asset

Another approach you may encounter is NIST SP 800–154, Guide to Data-Centric System Threat Modeling.

The publication focuses on protecting particular types of data as they move through and reside within systems. Instead of treating the application or server as the primary object of analysis, a data-centric approach asks where the data exists, how it moves, who is authorized to access it, and what security properties must be preserved.

Consider a system that processes customer records.

Those records might exist in a production database, an application cache, a message queue, a backup, an analytics pipeline, and a developer's test environment. Protecting the database alone does not protect the data throughout its lifecycle.

A data-centric threat model asks questions such as:

• Where can sensitive data be stored or processed?
• Which identities and services can access it?
• Can it be modified, copied, or disclosed outside authorized boundaries?
• What protections apply while it is transmitted, stored, or processed?
• Do those protections remain effective in backups, logs, and downstream systems?

This perspective is particularly useful for systems handling sensitive personal information, financial data, credentials, or regulated records.

One qualification: NIST SP 800–154 has been published as an initial public draft, and NIST's publication page has indicated plans to finalize it. It should therefore be treated as draft guidance unless its publication status has subsequently changed.

The broader lesson remains useful regardless of the document's status: threat modeling should follow the assets we need to protect, not just the infrastructure that happens to contain them.

LINDDUN — Because Security Is Not the Same as Privacy

LINDDUN is a threat-modeling methodology focused on privacy threats.

The name represents seven categories:

• Linkability
• Identifiability
• Non-repudiation
• Detectability
• Disclosure of information
• Unawareness
• Non-compliance

Traditional security threat modeling often emphasizes unauthorized access, modification, or disruption. Privacy analysis asks additional questions about whether people can be identified, tracked, profiled, or have their information used in ways they do not expect.

For example, an application might correctly authenticate users, enforce access controls, encrypt its database, and still create serious privacy problems.

It might retain identifiable activity logs indefinitely, expose browsing behavior to other parties, or combine information from different sources to infer sensitive characteristics.

Those issues may not be adequately addressed by a conventional STRIDE analysis alone.

LINDDUN is useful when privacy is a major design concern, particularly in consumer applications, identity systems, analytics platforms, and systems processing personal data.

It complements security-focused threat modeling rather than replacing it.

Attack Trees — Because We Need to Understand the Path, Not Just the Destination

Attack trees are another useful technique. They represent an attacker's objective as a hierarchy of possible ways to achieve it.

Suppose, the objective is to access another customer's account.

Possible branches might include:

• Steal or reuse the customer's credentials.
• Compromise the authentication flow.
• Exploit a broken authorization check.
• Steal a valid session token.
• Compromise an administrator account and abuse its privileges.

Each branch can be decomposed further. For example, stealing a session token might involve compromising a browser, exploiting insecure token storage, or intercepting a token through an inadequately protected channel.

Attack trees help make assumptions explicit and reveal where defensive controls can interrupt an attack.

They are particularly valuable for analyzing a small number of high-impact scenarios in depth. They can also be combined with STRIDE, PASTA, and ATT&CK.

OCTAVE — Because Not Every Threat Is an Application Bug

OCTAVE stands for Operationally Critical Threat, Asset, and Vulnerability Evaluation. It originated at Carnegie Mellon University's Software Engineering Institute.

OCTAVE takes an organizational perspective on information security risk. It emphasizes critical assets, operational requirements, organizational vulnerabilities, and the consequences of threats.

That makes it different from an approach primarily concerned with the technical design of an individual application.

An organization might have a perfectly reasonable application architecture but still face major risks because of inadequate incident-response procedures, weak operational controls, insufficient backups, or excessive dependence on a single provider.

OCTAVE helps bring those organizational and operational concerns into the analysis.

It is more relevant to enterprise-wide risk assessments than to a developer who needs to examine the design of one API endpoint.

These Approaches Solve Different Problems

It is tempting to compare all of these approaches as if they were competing products. In practice, they often address different parts of the problem.

The categories overlap, and this is not an exhaustive list. The important point is that they do not all answer the same question.

STRIDE and LINDDUN help identify different categories of threats. PASTA and OCTAVE provide more structured ways to organize risk analysis. Attack trees help explore attack paths. MITRE ATT&CK supplies a vocabulary for describing adversary behavior. Data-centric modeling changes the focus of the analysis to the data being protected.

A team can use more than one of these approaches without creating a contradictory threat model.

So Which Approach Should You Use?

For most application teams, I would start with a simple process:

1. Document the architecture. Identify users, services, trust boundaries, data stores, external dependencies, and important data flows.
2. Identify the threats. Use STRIDE to systematically examine components and interactions.
3. Identify the important assets. Determine what would happen if data were disclosed, modified, lost, or made unavailable.
4. Explore serious attack scenarios. Use attack trees or MITRE ATT&CK to investigate realistic paths to high-impact outcomes.
5. Assess and prioritize risk. Consider likelihood, impact, existing controls, and the cost of mitigation. Use a more structured approach such as PASTA when the system warrants it.
6. Add privacy analysis where appropriate. Use LINDDUN or equivalent privacy-focused analysis when identifying and protecting individuals is a significant concern.
7. Validate the mitigations. Turn the findings into architectural changes, security requirements, tests, monitoring, and operational procedures.

This does not mean every project needs every methodology. The goal is to choose the amount of rigor appropriate to the risks, not to accumulate acronyms.

A small internal application might need an architecture diagram, a STRIDE review, and a handful of concrete security requirements. A payment platform might justify deeper attack analysis, explicit business-risk assessment, privacy analysis, and mapping to adversary techniques.

The threat model should scale with the system's complexity and consequences of failure.

A Threat Model Is Not a Document You Complete and Forget

One final point is worth emphasizing.

Threat modeling is not particularly useful if its output is a spreadsheet that nobody consults after the design review.

The value comes from changing decisions. A threat should lead to a meaningful question about the architecture, a security requirement, a mitigation, a test, a detection, or an explicit decision to accept a risk.

And, those decisions need to be revisited when the system changes.

An authentication service that introduces a new federation provider has different trust boundaries and failure modes. An API that starts supporting delegated access has different authorization risks. A service that begins processing sensitive personal information has new privacy obligations.

The model needs to evolve alongside the system.

Conclusion

There is no single threat-modeling methodology that answers every security question.

STRIDE provides a practical starting point for identifying software threats. PASTA connects technical attack scenarios to business risk. MITRE ATT&CK helps describe realistic adversary behavior. Data-centric modeling focuses attention on protecting information throughout its lifecycle. LINDDUN addresses privacy, attack trees explore paths to attacker objectives, and OCTAVE expands the analysis to organizational risk.

The best approach is not necessarily the most comprehensive framework or the one with the most elaborate documentation.

It is the approach that helps a team discover meaningful threats, make defensible security decisions, and verify that the resulting controls actually work.

Because, the objective isn't to produce a threat model.

The objective is to build a system that is harder to compromise, easier to defend, and less likely to surprise us when something goes wrong.

Notes

• AI / GenAI / ChatGPT / etc were not used to generate the text of this article.
• ChatGPT was used to generate the images.
• I used em dashes in my writing before the current GenAI wave was a thing. Not planning on changing now.
• Names have been changed to protect the guilty.
• None of the hostnames or users used in examples actually exist.
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• I'm not responsible for the spontaneous combustion of the known universe or anything else bad that happens to you today as a result of your having read this blog post.