A Strategic Method for Developing Secure Systems: Threat Modelling

Introduction

Currently, cybersecurity involves more than just protecting against known attacks; it also involves foreseeing possible threats and creating systems that are resilient to them. Threat modeling is useful in this situation. Fundamentally, threat modeling is an organized method for determining, evaluating, and ranking security threats prior to their escalation into breaches. Organizations can use threat modeling to proactively incorporate security into systems, applications, and processes rather than responding to problems after they happen.

By addressing the following basic questions: What are we creating? What might go wrong? What are we doing to lessen it? Threat modeling gives business and technical executives a clear road map for striking a balance between resilience and innovation.

Use Cases of Threat Modelling

Threat modeling is not limited to a single industry or technological advancement. It can be modified for a number of situations, some of which are still relevant today:

1. Application Development: Finding flaws in an application's design and making sure secure programming practices are used prior to release.

2. Cloud Deployments: Outlining the dangers of incorrect setups, exposed data, or inadequate authentication and access restrictions.

3. IoT and Embedded Systems: Drawing attention to the dangers of gadgets with little processing power, like smart sensors or medical equipment, which are frequently used in exposed settings.

4. Artificial Intelligence (AI) Systems: Dealing with hostile inputs, data poisoning, or model manipulation that may compromise the integrity of decision-making.

Secured SDLC: Using STRIDE for Threat Modelling in Applications

One of the most common frameworks for threat modelling within the Secure Software Development Lifecycle (SDLC) is STRIDE—an acronym for six categories of threats:

• Spoofing Identity

• Tampering with Data

• Repudiation

• Information Disclosure

• Denial of Service

• Elevation of Privilege

By applying STRIDE at each phase of SDLC, organizations can anticipate specific risks and implement controls:

- Design Phase: Map out system architecture and data flows. Use STRIDE to examine attack surfaces (e.g., APIs, authentication mechanisms).

- Development Phase: Translate identified risks into security requirements (e.g., input validation, cryptographic controls).

- Testing Phase: Simulate threat scenarios derived from STRIDE to validate defenses against tampering, denial-of-service, or privilege escalation.

- Deployment Phase: Reassess STRIDE threats in production environments, ensuring patch management and monitoring strategies are in place.

AI-Specific Threat Modelling

Artificial Intelligence introduces unique challenges beyond traditional application risks. Since AI systems rely on data, algorithms, and models, threats often target the integrity and trustworthiness of these components. Common AI-specific risks include:

1. Data Poisoning: Malicious actors manipulate training data, leading the model to learn incorrect patterns.

2. Model Evasion: Attackers craft adversarial inputs to trick AI models (e.g., altering pixels in an image to bypass facial recognition).

3. Model Inversion: Attempts to reconstruct sensitive training data from model outputs.

4. Bias Exploitation: Attackers exploiting inherent biases in datasets, causing unreliable or unfair results.

AI threat modelling involves mapping the AI pipeline—from data collection to inference—and identifying where risks are most likely to occur. Security controls such as data validation, adversarial testing, and model explainability become crucial to maintaining trust.

AI Threat Modelling with Embedded/Edge Computing Security Best Practices

As AI increasingly runs on edge devices—such as autonomous vehicles, drones, or industrial IoT sensors—threat modelling must also consider constraints of embedded environments. These systems combine the risks of AI with hardware limitations and physical exposure.

Key Risks at the Edge:

- Limited computational power may restrict the use of strong encryption.

- Physical tampering with devices can compromise both AI models and data.

- Unreliable or insecure communication channels between edge devices and the cloud.

Best Practices for AI Threat Modelling in Embedded/Edge Systems:

1. Secure Boot & Firmware Integrity: Ensure devices boot only trusted code to prevent insertion of malicious models or firmware.

2. Lightweight Cryptography: Use resource-efficient encryption to protect data without overloading the processor.

3. Data Provenance & Validation: Verify the integrity of input data before AI inference to avoid poisoning attacks.

4. Hardware Root of Trust: Utilize secure elements or TPMs (Trusted Platform Modules) to safeguard keys and credentials.

5. Regular Threat Simulations: Continuously model how adversaries might exploit physical access, side-channel attacks, or communication weaknesses.

Conclusion

Threat modelling is not a one-time activity but a continuous discipline that evolves alongside technology. Whether applied to traditional applications through frameworks like STRIDE or to modern AI-driven edge systems, threat modelling enables organizations to anticipate risks, reduce attack surfaces, and design security into systems from the ground up.

In today’s digital landscape, where adversaries are faster and more sophisticated than ever, organizations that invest in proactive threat modelling gain a strategic advantage: they can innovate confidently while ensuring trust, compliance, and resilience remain intact.