Implementing Trust Boundary Models

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

One of the most interesting aspects of my last few roles, including my current work at Humain, is operating at the intersection of AI and advanced security/encryption techniques from zero-knowledge proof systems to the extension of Zero Trust principles into the agentic world. In traditional Zero Trust, we authenticate users and devices. In the agentic world, the “user” could be an autonomous agent — a system that reasons, acts, and interacts with data and other agents, often at machine speed. That changes everything. To secure this new ecosystem, Zero Trust must evolve from static identity verification to dynamic trust orchestration, where every action, decision, and data exchange is continuously verified, contextual, and cryptographically enforced. 1. Agent Identity and Attestation Every agent must have a verifiable, cryptographically signed identity and prove its integrity at runtime; not just who you are, but what you’re running: the model, weights, policy context, and data provenance. 2. Intent-Aware Policy Enforcement Access control must become intent-aware, so agents act only within bounded policy domains defined by explicit goals, permissions, and ethical constraints — continuously verified by embedded governance logic. 3. Least Privilege and Time-Bound Access Agents must operate under least privilege, with access granted only for the minimum scope and durationrequired. In fast-moving agentic environments, time-limited trust becomes an essential safeguard. 4. Assumed Breach and Blast Radius Containment We must assume some agents or environments will be compromised. Security design should minimise impact through microsegmentation, strict trust boundaries, and dynamic reassessment of communication between agents. 5. Encrypted Cognition As models process sensitive data, confidential AI becomes essential where combining homomorphic encryption, secure enclaves, and multi-party computation can ensure that the model cannot “see” the data it processes. Zero Trust now extends into the reasoning process itself. 6. Adaptive Trust Graphs Agents, services, and humans form dynamic trust graphs that evolve based on behaviour and context. Continuous telemetry and anomaly detection allow these graphs to adjust privileges in real time based on risk. 7. Cryptographic Provenance Every output, decision, summary, or recommendation must be traceable back to the data, model, and policy that produced it. Provenance becomes the new perimeter. 8. Autonomous Audit and Forensics Every action should be self-auditing, cryptographically signed, and non-repudiable forming the foundation for verifiable operations and compliance. 9. Machine-to-Machine Governance As agents begin to negotiate, transact, and collaborate, Zero Trust must extend into inter-agent diplomacy, embedding ethics, accountability, and policy directly into machine communication. If you’re working on AI security, agent governance, or confidential computation, I’d love to connect.

🚨 Advanced Threat Modeling — A Security Architect’s Ultimate Playbook 🛡️📘 After years of working across critical security domains, I created a comprehensive guide to help teams go beyond the basics and build threat modeling into the fabric of their systems. 🔍 What’s Inside the Guide? 🧠 Core Principles → Systematic analysis → Attacker mindset → Early integration into the SDLC 📐 Advanced Methodologies ✔ STRIDE-per-element threat analysis ✔ PASTA risk-centric modeling ✔ DREAD scoring for prioritization ✔ Attack Trees for layered adversary simulation 🧰 Implementation Techniques → System decomposition and trust boundaries → Threat Model as Code (YAML-based) → Integration with DevSecOps workflows → Real-world scenario development and testing ⚙️ Tooling & Automation → Tool comparisons (Microsoft TMT, Threat Dragon, IriusRisk, ThreatModeler) → Automated threat identification → Version-controlled models for CI/CD pipelines 📊 Case Studies & Results → Financial sector: 35% fewer prod vulnerabilities → Healthcare: 40% drop in security incidents → Measurable ROI in compliance & remediation 📈 Why It Matters: Threat modeling isn’t just a design-phase checkbox — it’s your blueprint for proactive, adaptive security across every layer of the stack. 📥 Want the full 20+ page PDF? Drop a “📘” in the comments or DM me — happy to share it! 🧠 Curious: What’s your go-to framework — STRIDE, PASTA, OCTAVE, LINDDUN, or something else? #ThreatModeling #CyberSecurity #SecurityArchitecture #STRIDE #DevSecOps #AppSec #OWASP #RiskAssessment #SecurityEngineering #SDLC #BlueTeam #InfoSec #AttackTrees #SecurityTools #ThreatIntel #OkanYILDIZ

AI security is entering a new phase, one where the systems protect themselves. The A2AS: Agentic AI Runtime Security and Self-Defense paper makes that argument with quiet conviction. Instead of relying on filters, wrappers, or fine-tuning, it proposes a framework where large language models can verify, authenticate, and defend their own reasoning. The idea is as pragmatic as it is radical and that is to make AI secure by design, not by supervision. What the paper outlines: • The BASIC security model, a framework of five controls: Behavior Certificates, Authenticated Prompts, Security Boundaries, In-Context Defenses, and Codified Policies. Each addresses a different risk surface from behavior drift to malicious prompt injection. • Three design pillars: runtime, self-defense, and self-sufficiency, ensuring that protection happens in real time, leverages the model’s reasoning, and minimizes dependency on external systems. • The A2AS framework, which implements BASIC as a runtime layer much like HTTPS secures HTTP, embedding trust directly into how models operate. Why this matters AI agents now operate across critical domains, from finance to infrastructure. Their greatest vulnerability lies in how they process both trusted and untrusted data inside the same context window. This design flaw enables prompt injection attacks that manipulate instructions or extract data. Existing defenses rely on external filters, retraining, or sandboxing, each adding complexity or latency. A2AS, by contrast, uses the model’s own reasoning to authenticate and protect itself at runtime. Key risks and practices: • Behavior drift and misuse are limited by Behavior Certificates that define and enforce permissions. • Tampered inputs are blocked through Authenticated Prompts that verify content integrity and attribution. • Context mixing and indirect injections are mitigated by Security Boundaries that tag untrusted inputs. • Unsafe reasoning is restrained by In-Context Defenses embedded in the prompt itself. • Compliance and governance are maintained through Codified Policies that enforce business rules as executable code. Who should act: Security architects, AI platform engineers, and governance teams can adopt A2AS as a baseline for runtime defense. It requires no retraining or architecture overhaul, yet creates a measurable layer of assurance. Action items: • Use the BASIC model as a checklist for every new agent or LLM integration. • Issue Behavior Certificates for all agents and enforce them at runtime. • Add Authenticated Prompts and Security Boundaries to instrument context. • Embed In-Context Defenses and Codified Policies to maintain safe reasoning. • Regularly audit and adapt configurations as new attack patterns evolve.

#Operational #Technology is becoming more connected, bringing efficiency and visibility, but also expanding the attack surface. Key takeaways from recent multi-agency #guidance on securing #OT #connectivity: ▪︎ Start with a documented business case for every OT connection: requirement, benefits, acceptable risk, potential impacts, new dependencies, and senior risk ownership. ▪︎ Treat obsolete/unsupported assets as untrusted. Use segmentation and compensating controls as temporary measures, with a roadmap to replace. ▪︎ Design for operational resilience: avoid fragile dependencies, identify single points of failure, and ensure manual fallback where needed. ▪︎ Limit exposure by default: ○ Prefer just-in-time access (enable connectivity only when required). ○ Ensure OT connections are outbound-initiated; avoid inbound port exposure. ○ Use brokered access via a DMZ for third parties/remote support—never direct internet-to-OT. ▪︎ Centralise and standardise remote connectivity: reusable patterns reduce misconfiguration risk and simplify monitoring and change control. ▪︎ Use secure, standardised protocols and validate traffic: ○ Migrate to secure industrial protocol variants where available. ○ Enforce “known good” **schema/protocol validation** at trust boundaries. ▪︎ Harden the OT boundary with defence-in-depth: patchable boundary assets, remove unused services, phishing-resistant #MFA, no default passwords, least privilege, context-aware access, and (where appropriate) **unidirectional flow controls. ▪︎ Assume compromise is possible: apply zoning/micro-segmentation, separation of duties, and strong boundary controls to limit contamination and lateral movement. ▪︎ Log and monitor all connectivity: baseline “normal,” monitor inter-zone flows, and treat **break-glass use as a highest-severity alert**. ▪︎ Have a tested isolation plan: from full site isolation to service-specific isolation; consider maintaining trusted one-way telemetry/log flows during incidents.

How to Threat Model AI Systems: Threat modeling AI systems starts with one mistake: Treating the model as the asset. The model is rarely the problem. Reach is. AI systems fail where they connect to data, tools, and workflows. Start by mapping the execution path: - User input - Prompt or agent logic - Context retrieval (RAG) - Tool invocation (MCP, APIs) - Action execution Every arrow in that diagram is a trust boundary. Next, identify authority: - What data can be read? - What actions can be taken? - What persists across sessions? Assume interpretation will fail. Design controls that do not. Finally, model blast radius: - What happens if context is poisoned? - What if the wrong tool is invoked? - Can actions be reversed? Classic frameworks still apply. The components just changed names. Threat model where AI can reach, not just how smart it is. How are you threat modeling AI systems?

The industry continues to rally around the importance of deterministic architectural boundaries for Agentic AI security. Probabilistic safety mechanisms alone are not enough, trustworthy agentic AI requires deterministic architectural boundaries. The paper introduces the "Trinity Defense Architecture", a three-layer enforcement model built on the premise that no amount of prompt engineering, RLHF, or alignment fine-tuning can provide hard security guarantees for autonomous agents. - A Capability Boundary Layer that restricts what tools, APIs, and resources an agent can access, enforced at the infrastructure level before the LLM ever processes a request - An Information Flow Boundary Layer that controls data movement between trust domains using mandatory access controls, preventing sensitive data from leaking regardless of model output - An Authority Boundary Layer that governs delegation chains with cryptographic verification, ensuring authority attenuates at every hop rather than amplifying The core insight is that LLM alignment operates at the semantic level while security must be enforced at the architectural level. The industry is leaning heavily on classifiers, guardrails, and prompt-based protections, but those are valuable but not deterministic. Anthropic's own Claude Code Auto Mode data shows a 17% miss rate on dangerous actions. That is meaningfully better than no controls, but it is not a hard boundary. Hard boundaries provide the floor, probabilistic controls provide depth. You need both, but you cannot build a security program for agents on probabilistic mechanisms alone. https://lnkd.in/e_wz3UZV