Cybersecurity Threat Modeling

How to Learn Threat Modeling Without Overcomplicating It Threat modeling doesn’t need to be complex. Too many professionals get stuck trying to follow rigid frameworks, overusing tools, or treating it as a one-time exercise. The reality? Threat modeling is about structured thinking, not fancy tools. A Simple Approach to Get Started 👇 1 - What Are You Protecting? ↳ Identify the critical assets—data, applications, cloud workloads, or identities—that need protection. 2 - What Can Go Wrong? ↳ Think like an attacker. What are the biggest threats to those assets? Examples: - Unauthenticated API access - Misconfigured IAM roles - Insider threats 3 - What Are You Doing About It? ↳ Map out existing security controls and identify gaps. Do you have IAM restrictions? Monitoring? Encryption? If a control fails, what happens next? 4 - What Needs to Improve? ↳ No system is perfectly secure. Identify mitigations and prioritize based on risk. Sometimes, simpler fixes (like better logging or MFA) are more effective than complex tools. Common Mistakes to Avoid 1 - Overusing Tools Instead of Thinking Critically ↳ Threat modeling is not about running a tool and getting a report. Tools can help visualize threats, but they don’t replace human judgment. 2 - Trying to Model Every Possible Threat ↳ Focus on the most likely and impactful threats, not creating an exhaustive list of every theoretical risk. 3 - Doing It Once and Forgetting About It ↳ Threat modeling is not a one-time exercise. Your security landscape evolves, and so should your threat models. Focus on structured thinking, avoid overcomplicating the process, and iterate as you go. Good luck on your threat modeling journey !

I am a Security Engineer at Google with 7+ years of experience. Here are 17 lessons I learned about Threat Modelling working in DevSecOps that made me a better Security Engineer... (It took me a lot of mistakes to learn these, but you don't have to!) 1. Threat modelling starts with the business → if you don’t know what makes money, keeps trust, or keeps systems up, your model is just a diagram, not risk. 2. Draw the system before you “secure” it → users, services, queues, third parties, data stores, and which way data flows; no diagram = fake clarity. 3. Trust boundaries are where real trouble lives → anywhere data or control crosses teams, networks, orgs, or privilege levels deserves extra attention. 4. Model the attackers you actually face → insiders, leaked tokens, overprivileged services, abused workflows are more likely than nation-state zero days. 5. Threat modelling belongs in design docs → if it happens after everything is built, you’re just writing an incident report in advance. 6. Architecture is a security decision → multi-tenant vs single-tenant, shared DB vs per-tenant DB, sync vs async all change which attacks are even possible. 7. Your CI/CD and IaC repos are part of the attack surface → build agents, runners, deployment keys, and pipelines should be on the diagram, not an afterthought. 8. Business logic is where attackers quietly print money → refunds, credits, retries, limits, and edge cases need more modelling than your login page. 9. Good threat models are about assumptions → “only service X can call this API” or “this key never leaves the VPC” should be written down and challenged. 10. A threat model without concrete controls is just a story → each high-risk scenario should end in specific changes to design, config, or process. 11. Prevention without detection is half a job → for every serious threat, ask “how would we know this is happening” and “who gets paged.” 12. You can’t fix everything → be explicit about what you accept, why, and who agreed; unspoken risk is what hurts you later. 13. People and process can undo perfect design → who can approve access, hotfix in prod, change configs, and bypass checks must be part of the model. 14. Complexity hides vulnerabilities → if it takes 20 minutes to explain the data flow, you’re probably missing risks and nobody will maintain the controls. 15. Reuse threat patterns for common flows → login, file upload, webhooks, internal admin tools should have standard risks and standard mitigations you pull from. 16. The best sessions feel like debugging, not a police interview → engineers should walk out feeling “we found landmines together,” not “security blocked us again.” 17. Threat modelling is a habit, not an event → bake a small threat section into every big design and major change; repetition beats a once-a-year workshop. -- 📢 Follow saed ‎for more ♻️ share the insights

The OWASP® Foundation Threat and Safeguard Matrix (TaSM) is designed to provide a structured, action-oriented approach to cybersecurity planning. This work on the OWASP website by Ross Young explains how to use the OWASP TaSM and as it relates to GenAI risks: https://lnkd.in/g3ZRypWw These new risks require organizations to think beyond traditional cybersecurity threats and focus on new vulnerabilities specific to AI systems. * * * How to use the TaSM in general: 1) Identify Major Threats - Begin by listing your organization’s key risks. Include common threats like web application attacks, phishing, third-party data breaches, supply chain attacks, and DoS attacks and unique threats, such as insider risks or fraud. - Use frameworks like STRIDE-LM or NIST 800-30 to explore detailed scenarios. 2) Map Threats to NIST Cybersecurity Functions Align each threat with the NIST functions: Identify, Protect, Detect, Respond, and Recover. 3) Define Safeguards Mitigate threats by implementing safeguards in 3 areas: - People: Training and awareness programs. - Processes: Policies and operational procedures. - Technology: Tools like firewalls, encryption, and antivirus. 4) Add Metrics to Track Progress - Attach measurable goals to safeguards. - Summarize metrics into a report for leadership. Include KPIs to show successes, challenges, and next steps. 5) Monitor and Adjust Regularly review metrics, identify gaps, and adjust strategies. Use trends to prioritize improvements and investments. 6) Communicate Results Present a concise summary of progress, gaps, and actionable next steps to leadership, ensuring alignment with organizational goals. * * * The TaSM can be expanded for Risk Committees by adding a column to list each department’s top 3-5 threats. This allows the committee to evaluate risks across the company and ensure they are mitigated in a collaborative way. E.g., Cyber can work with HR to train employees and with Legal to ensure compliance when addressing phishing attacks that harm the brand. * * * How the TaSM connects to GenAI risks: The TaSM can be used to address AI-related risks by systematically mapping specific GenAI threats - such as sensitive data leaks, malicious AI supply chains, hallucinated promises, data overexposure, AI misuse, unethical recommendations, and bias-fueled liability - to appropriate safeguards. Focus on the top 3-4 AI threats most critical to your business and use the TaSM to outline safeguards for these high-priority risks, e.g.: - Identify: Audit systems and data usage to understand vulnerabilities. - Protect: Enforce policies, restrict access, and train employees on safe AI usage. - Detect: Monitor for unauthorized data uploads or unusual AI behavior. - Respond: Define incident response plans for managing AI-related breaches or misuse. - Recover: Develop plans to retrain models, address bias, or mitigate legal fallout.

So you think you know how to threat model? Many SOCs claim to do formal threat modeling (whether they really do is another story). But let’s talk about the right way–because a half-baked threat model can be worse than none at all, especially when it comes to organization risk. 𝟭. Introspection: Know your business–and its risk • Identify the crown jewels: Which assets, if compromised, would cripple your operations or reputation? • Spiral method: Envision a crime scene–except it hasn’t happened yet (hopefully). Start at your most critical points and circle outward, noting controls in place. • Map your processes: Understand your dependencies, supply chain links, and workflows to figure out where the real business risk lies. 𝟮. Extrospection: Know your threat landscape • Threat actors 101: Who’s targeting your vertical? How do they operate–ransomware, data exfil, or something else? • Outcomes & motives: Whether it's a quick payday or long-term espionage, each threat actor’s endgame shifts your risk profile. • Worst-case mindset: If they succeed, what’s the impact on revenue, reputation, or compliance? 𝟯. Union: Combine Business & Threat Risk • Introspection + Extrospection: Once you see your weaknesses and adversaries' strengths, theoretically set fire to your own org to find the flashpoints. • Prioritize by Risk: Not all threats matter equally. Tackle high-likelihood, high-impact scenarios first. • Feed it back: These insights drive your detection engineering–especially behavioral and sequential detections that address the most significant threats. 𝟰. Evolve: Threat Modeling is Never Done • Track & Iterate: Each exercise introduces new defenses (lowering some risks) and may uncover new attack paths (introducing others). • Stay Current: New business ops, acquisitions, or tech adoptions all shift your threat landscape. Revisit your model regularly. • Continuous Improvement: Capture lessons learned, adjust your controls, and refine your detection logic to stay in step with reality. Threat modeling isn’t just a one-off workshop–it’s a cycle that guides strategic security decisions and aligns detection capabilities with genuine business risk. How do you keep your threat model updated as the business and threat landscape evolve?

Ever wonder how companies like Rippling , Amazon or Meta build secure products at scale? Most teams wait until something breaks to think about security.That’s backwards. The smart ones — like Amazon, Meta, or Rippling — start with threat modeling. When I was at Rippling, this mindset was baked into how we built; not as a checklist, but as part of the design process. Here’s how to do it without any fancy tools: ✍️ 1. Map the System • Draw out the components of your app or feature. • Include APIs, user flows, databases, 3rd parties, etc. • Make sure you define trust boundaries (e.g. frontend ↔ backend, internal ↔ external). 🔍 2. Ask Key Questions Use frameworks like STRIDE or just ask: • What are we protecting? • What could go wrong? • Who might attack it? • How might they succeed? • What happens if they do? ⚠️ 3. Spot Threats • Look at entry points (login, uploads, APIs). • Think like an attacker: where’s the weak link? • Don’t forget non-obvious areas like audit logs or admin tools. ✅ 4. Mitigate + Document • Decide how you’ll reduce each risk. • Add controls: validation, auth, logging, rate limits, etc. • Track open threats like you track bugs — don’t just “note them.” That’s manual threat modeling : simple, powerful, and timeless. Now, if you want to automate and operationalize this across a fast-moving team? I use HackerScope (link in first comment). It lets you: • Visually map threat models • Collaborate with eng, product & security • Auto-track threats over time • Make a checklist of ToDos to ensure all the gaps are filled. It’s like having a living threat model inside your dev workflow. Security shouldn’t feel like homework. It should feel like design. #ThreatModeling #Cybersecurity #AppSec #HackerScope #SecureByDesign #StartupSecurity #EngineeringExcellence #ProductSecurity

Threat Modeling: Proactively Protecting Medical Devices from Cyber Attacks In today’s digital healthcare landscape, medical devices are increasingly targeted by cyber threats that can compromise patient safety and data integrity. Threat modeling is a proactive strategy that enables manufacturers to anticipate potential cyber attacks and implement effective countermeasures. What is Threat Modeling? Threat modeling is a structured methodology for identifying, assessing, and mitigating cybersecurity threats within a system. It involves: 📝 Defining Scope and Objectives: Outlining the system’s boundaries and security goals. 💎 Identifying Assets and Threats: Determining valuable assets (like patient data and device functionality) and recognizing potential threats. Analyzing Threats Using STRIDE Methodology 👤 Spoofing: Impersonation of entities to gain unauthorized access. 🛠️ Tampering: Unauthorized alteration of data or code. 🚫 Repudiation: Denial of actions to avoid accountability. 🔒 Information Disclosure: Exposure of confidential information. ❌ Denial of Service: Disruption of device services. 🔓 Elevation of Privilege: Unauthorized gain of higher access levels. 🛡️ Mitigating Threats: Implementing strategies and controls to address identified threats. Why Threat Modeling is Critical By systematically analyzing potential threats, manufacturers can: 🔍 Anticipate Vulnerabilities: Identifying weaknesses before they can be exploited. 🔐 Enhance Security Measures: Implementing targeted controls to mitigate risks. 📜 Ensure Regulatory Compliance: The FDA mandates threat modeling as part of cybersecurity documentation for cyber devices. 🩺 Protect Patient Safety: Preventing cyber attacks that could impact device performance and patient care. Adopting threat modeling is not just about meeting regulatory requirements; it’s about proactively defending your medical devices in an ever-evolving cyber threat landscape. This approach strengthens overall device security and fosters greater trust among users and patients. #MedicalDevices #FDA #AI

Most AI security programs protect the wrong thing 🛡️ Traditional cybersecurity is built around the network perimeter, keeping attackers out, protecting the data inside, detecting intrusions when they happen. AI systems introduce a different attack surface. The model itself is the target. The training data is the target. The inference pipeline is the target. Let's look at the three attack categories every GRC and security team needs to understand now. 👇 1️⃣ Data Poisoning: An adversary introduces manipulated data into the training set, causing the model to learn incorrect patterns or develop hidden behaviors that activate under specific conditions. The most dangerous variant is the backdoor attack, in which the model performs normally on clean inputs and passes every standard accuracy test, then fails in predictable, attacker-controlled ways when triggered by a specific input pattern. The governance failure mode is subtle. Poisoned models look fine in testing. The gap between "model passed evaluation" and "model is safe to deploy" is exactly where data governance lives. 2️⃣ Prompt Injection: The defining security threat of LLM deployment. An attacker embeds malicious instructions in content the model processes, a user message, a retrieved document, a webpage, that override the model's intended behavior. Indirect injection is the more dangerous variant. The model retrieves attacker-controlled content during operation, redirecting its actions without the user or operator knowing. 💡 Agentic AI systems are particularly exposed. A model that can take actions, send emails, query databases, or execute code is one where a successful prompt injection becomes an execution vector, not just an output problem. 3️⃣ Model Extraction: An attacker queries a deployed model repeatedly, observing inputs and outputs, and uses those observations to reconstruct a functional replica. The replica can compete commercially, enable adversarial attacks offline, or reveal vulnerabilities exploitable against the original. This is an intellectual property and security risk simultaneously. The attack is difficult to detect because it looks like normal API usage. What makes these different from traditional cybersecurity risks is that they target the AI system's behavior and integrity, not just surrounding infrastructure. A firewall doesn't stop a poisoned training set. Endpoint detection doesn't catch prompt injection in a retrieved document. Organizations need AI-specific threat modeling, not traditional controls applied to AI deployments. MITRE ATLAS maps these attacks in detail. OWASP's LLM Top 10 is a good starting list: https://lnkd.in/g3ZRuZNq Drop a comment and let me know which of these three attack categories you need more to learn more about! #AIGovernance #AIRisk #Cybersecurity #GRC #AI

Cyber Performance Goals (CPGs): What are they? Why should we care? 🤷♂️ Every organisation, regardless of industry or location, faces unique cyber threats. Traditional frameworks like #CIS, #ISO, and #NIST are a good starting point for security guidance, but they often lack clear connections between real threats, adversary attack techniques, and the associated mitigations. This is where Cyber Performance Goals come in. CPGs bridge that gap through traceability and practical application by starting with a good security outcome, linking this to a valid risk or TTP, and providing the recommended action to address the key risk(s). In CISA's words, “The CPGs are voluntary practices with high-impact security actions that outline the highest-priority baseline that measures businesses and critical infrastructure owners of all sizes can take to protect themselves against cyber threats.” ⚡ Enhanced Cyber Performance Goals (eCPGs). CPGs alone are an excellent resource for understanding how to achieve secure outcomes, but using them in isolation won’t do much without the necessary business context. Here are my insights when working with CPGs in real-world engagements: 💡 Use Threat Events (as defined in NIST 800-31r1) - Sector-Specific Scenarios: Identify realistic threats and attack vectors relevant to your industry/organisation. - E.g. Threat = "Steal valid customer account information/online banking credentials". (Financial Services) - Threat Modeling: Identify and map potential attack paths and initial access vectors within your high-value assets and hosting environments. - Risk Prioritisation: Focus on high-impact, high-likelihood scenarios first. 💡 Vulnerability and Weakness Mapping (CVEs / CWEs) Before an attack can be successful, there must be a vulnerability or weakness. This part is crucial for validating any downstream attack TTPs and mitigating controls. - Example: Threat = "Steal valid customer account information/online banking credentials". ➡️ Weakness = "CWE-306: Missing Authentication for Critical Function", “CWE-308: Use of Single-factor Authentication". 💡 Link To Cyber Performance Goals (CPGs) - Leverage existing CPGs as adequate mitigating controls. - MITRE ATT&CK Alignment: CPGs already map ATT&CK TTPs to recommendations for threat-informed risk mitigation. - NIST CSF Compliance: Helps ensure control standards alignment for organisations that use NIST. 💡 Bringing it all together This might seem like a lot of effort, but in practice, it’s very straightforward once you understand the threats and weaknesses facing a target organisation. Using these CPGs with this approach gives your impact assessments and control recommendations a lot more credibility when they come from reputable and threat-informed sources, not just you. Check out the complete list of CPGs here: https://lnkd.in/gdTQ_n_W #cybersecurity #performance #goals #cpgs #threatintelligence #CISA #NIST #mitreattack

🚨🧠 LLM TOOLS FOR CYBERSECURITY: the tool isn’t the threat — the workflow is I’m seeing a wave of “cyber AI” assistants that can plan, chain tasks, and plug into real tooling. That can boost productivity for authorized security work… But it also changes your threat model because these systems bring agency: memory, automation, and tool access. Here’s what these “Top LLM Tools for Cybersecurity” posts are really telling us 👇 ⚠️ Capability Compression — recon + reasoning + reporting becomes “one interface” ➤ Defense: Treat AI-assisted workflows like privileged tooling (same controls as admin tools). ⚠️ Prompt → Action Bridges — when an assistant can trigger tools, mistakes become incidents ➤ Defense: Approval gates for high-risk actions + allowlisted operations only. ⚠️ Data Spill Risk — pasting targets, logs, creds, screenshots into assistants can leak sensitive context ➤ Defense: Redaction by default + data boundaries + self-hosted options for regulated work. ⚠️ Reproducibility Gap — the model gives “answers,” but teams can’t prove how it got there ➤ Defense: Audit-grade logging (prompts, tool calls, outputs) + change control. ⚠️ Model Drift / Tool Drift — same prompt, different day, different result ➤ Defense: Version pinning + evaluation sets + regression tests for workflows. ⚠️ Misuse Risk — dual-use tools get repurposed outside authorized scope ➤ Defense: Strong identity, policy enforcement, rate limits, and environment isolation. ✅ How to use these tools responsibly (quick rule): Use them to summarize, triage, document, map to frameworks (MITRE/OWASP), and generate checklists — not to automate “actions” without guardrails. 👉 If one of these AI tools was plugged into your environment today, would you be able to answer: Who used it? What data went in? What actions did it trigger? What changed in the system because of it? #CyberSecurity #AISecurity #LLMSecurity #SecurityEngineering #AppSec #DevSecOps #ThreatModeling #ZeroTrust #IdentitySecurity #SecurityArchitecture #SecOps #Governance

STRIDE isn't enough for threat modeling AI systems. Not even close. Here's what your AI threat models are missing: 1. Probabilistic attack surfaces - Your threat model must account for confidence thresholds and statistical manipulation 2. Prompt engineering attacks - Users can weaponize the very interface you provide 3. Knowledge extraction vulnerabilities - Every interaction is potentially revealing your training data or proprietary knowledge 4. Context contamination - Who's validating what gets pulled into your model's context window? 5. Emergent behaviors - The most dangerous vulnerabilities are those that arise from legitimate features interacting Security teams that apply yesterday's frameworks to today's AI systems are creating a false sense of security.