Managing System Integration and Emerging Risks

Banks’ risk management is often too reactive due to the fact that many banks still rely on fragmented data systems and manual reviews, making it difficult to detect early warning signs and trends. Additionally, the sheer volume and pace of regulatory changes make it hard for banks to anticipate and adapt quickly, leading to compliance issues being addressed after the fact rather than proactive. Reactive strategies often tie up resources that could be used for growth or innovation, as staff are diverted to deal with emerging problems instead of preventing them. Insufficient adoption of advanced analytics and automation prevents banks from continuously monitoring risks and learning from past incidents, which would otherwise support a proactive approach. But HRO (High Reliability Organization) principles can offer a structured framework to transform banks from reactive risk managers into resilient, antifragile institutions by addressing systemic weaknesses in culture, processes, and decision-making. HROs treat near misses and minor errors as critical indicators of systemic vulnerabilities. For banks, this means continuous monitoring of emerging threats (e.g., cyber risks, liquidity mismatches) rather than waiting for regulatory penalties or crises. By learning from small failures, banks adapt processes to withstand larger shocks, turning volatility into a source of improvement. HROs reject oversimplified explanations for risks, forcing deeper analysis. Addressing underlying issues like siloed data or flawed incentive structures instead of temporary fixes. Banks would design systems to handle interconnected risks (e.g., climate-linked credit defaults) rather than compartmentalizing them. Real-time awareness of frontline activities enables rapid response. Branch managers or traders with situational expertise can escalate risks immediately, bypassing bureaucratic delays. Shifting capital or personnel to emerging hotspots (e.g., fraud spikes) prevents crises from escalating. HROs build systems that adapt under stress can with regularly simulating black-swan events (e.g., AI-driven market collapses) to refine contingency plans. Balancing cost efficiency with fail-safes (e.g., backup liquidity pools) to avoid over-optimization fragility Prioritizing knowledge over hierarchy flattens power dynamics. For example, risk analysts or compliance officers can override outdated protocols during fast-moving crises. Encouraging open reporting of errors without blame reduces cover-ups and fosters innovation. HRO principles align with Nassim Taleb’s antifragility concept by institutionalizing mechanisms to gain strength from volatility. Near-miss data feeds into predictive models, improving risk forecasts. Regulatory compliance becomes a feedback loop for improvement rather than a checkbox exercise.

🚀 My latest research "Cognitive Integration Process for Harmonising Emerging Risks" is now published in the Journal of AI, Robotics and Workplace Automation. 95% of Australian businesses are SMEs operating on ~$500 cybersecurity budgets. Yet they're being asked to securely integrate AI, quantum computing, and blockchain into their operations. How do you make sound security decisions about emerging technologies when you lack both technical expertise and enterprise-level resources? This is fundamentally a systems engineering challenge that requires first principles thinking. When I presented this research at the Programmable Software Developers Conference in Melbourne in March, I asked the room: "Heard of an AI security incident?" No hands up. "Would you know what an AI security incident looked like?" No hands. This illustrates the gap between AI hype and foundational security understanding - the first principles are missing. That's why I developed CIPHER (Cognitive Integration Process for Harmonising Emerging Risks) - a cognitive mental model that applies systems thinking to technology integration in resource-constrained environments. 🧠 Six cognitive stages: Contextualise, Identify, Prioritise, Harmonise, Evaluate, Refine 🔧 Systems engineering foundation: Built on cognitive science, game theory, and dynamical systems theory 🎯 Technology agnostic: Works across any emerging technology, any environment, any resource constraint CIPHER is a cybersecurity framework that gives smaller organisations the same strategic decision-making capabilities that large enterprises use, designed for their operational realities. It bridges the gap between cutting-edge security research and the practical constraints that define how most Australian businesses operate. The framework recognises that in resource-constrained environments, enterprise security models cannot be applied at scale. You need cognitive tools that help teams think systematically in complex integration challenges without requiring extensive technical depth or large security budgets. My research journey continues: I'm now deep into my UNSW Canberra Masters Research capstone, building on my 2023 work on LLMs in SME cybersecurity. The goal? Developing specialised security models and creating an agnostic, holistic measurement framework for LLMs in Australian SMEs - essentially taking the $500 problem from 2023 into the AI-driven reality of 2025. #CyberSecurity #SystemsEngineering #SME #Australia #AI #EmergingTech #ResourceConstrainedSecurity #CIPHER #FirstPrinciples

As organizations transition from pilots to enterprise-wide deployment of Generative and Agentic AI, it's crucial to recognize that GAI risks differ significantly from traditional software risks. Towards that, it is important to go back to basics and this publication from 2024 by National Institute of Standards and Technology (NIST)'s Generative AI Profile does a great job! 🌐 Here are the four highest-impact risks and the mitigation actions every organization should implement:- 1. Systemic Risk: Algorithmic Monocultures & Ecosystem-Level Failures When multiple industries depend on the same foundation models, a single unexpected model behavior can lead to correlated failures across the ecosystem. ⚡ Mitigation: - - Build model diversity and avoid single-model dependencies. - Maintain fallback systems and contingency workflows. - Apply stress tests that simulate sector-wide shocks. 2. Human-Originating Risks (Misuse, Over-Trust, Manipulation) Many GAI incidents stem from human behavior, including misuse, over-reliance, indirect prompt injection, and flawed assumptions. ⚡ Mitigation:- - Implement continuous user education on limitations and safe use. - Enforce access controls, privilege separation, and plugin vetting. - Maintain audit trails and logging to identify misuse early. 3. Content Integrity Risks (Hallucinations, Synthetic Media, Provenance Failure) GAI increases the scale and believability of fabricated content, from medical misinformation to deepfake-enabled harms. ⚡ Mitigation:- - Invest in content provenance, watermarking, and metadata tracking. - Require pre-deployment testing for hallucination profiles across contexts. - Use cross-model verification before high-stakes outputs are acted upon. 4. Security Risks (Prompt Injection, Data Leakage, Model Extraction) NIST highlights increasingly sophisticated attack surfaces unique to LLMs: indirect prompt injection, data extraction, and plugin-initiated compromise. ⚡ Mitigation:- - Apply secure-by-design reviews for all LLM integration points. - Red-team regularly using GAI-specific attack methods. - Log inputs/outputs via incident-ready documentation so breaches can be traced. 🔐 The bottom line:- AI risk management is not a technical afterthought, it is now a core capability. Organizations that operationalize governance, provenance, testing, and incident disclosure (NIST’s four focus pillars) will be the ones that deploy AI safely and at scale. 💬 If you’d like to explore Gen AI and Agentic AI risks, practical mitigation strategies, or how to operationalize the NIST AI RMF for your organization, feel free to comment or DM. Let’s build safer AI systems together! #AI #GenAI #AIGovernance #NIST #AIRMF #RiskManagement #AITrust #ResponsibleAI #AILeadership

🤔 How can large, complex projects navigate the complexities of systems integration? We introduce the concept of "disciplined flexibility" as a strategic approach to maintain stability while adapting to evolving challenges throughout the project lifecycle in "The dynamics of systems integration: Balancing stability and change on London's Crossrail project", coauthored with Kesavan Muruganandan Andrew Davies and Juliano Denicol in International Journal of Project Management 🔗 Read the full article https://lnkd.in/gVw8spkk (CC BY license) Key Insights: 🔹 "Disciplined flexibility" as a dynamic process of maintaining stability, while responding flexibly to changing conditions 🔹 Challenge of complex systems with interdependent systems at different degrees of maturity. 🔹 Strategies for ongoing monitoring and control to ensure successful integration. 🔹 Reciprocal interdependencies at both system and system-of-systems levels. Abstract Systems integration is essential for the design and execution of large, complex projects, but relatively little is known about how this task develops over time during the life cycle of a project. This paper builds on the concept of “disciplined flexibility” to describe how systems integration can be conceived as a dynamic process of maintaining stability, while responding flexibly to changing conditions. We examine the dynamics of systems integration through a case-study of Crossrail, the construction of London’s new urban railway system, which will be called the Elizabeth Line when it opens for service. The balancing act of stability and change manifests during critical periods of the project life cycle as various interdependent systems evolve with different degrees of maturity. We identify how various types of reciprocal interdependencies in complex projects such as Crossrail – at the system and system of systems levels – require ongoing monitoring and control, and the mutual adjustment of tasks.  Reference as: Muruganandan, K., Davies, A., Denicol, J., & Whyte, J. (2022). The dynamics of systems integration: Balancing stability and change on London's Crossrail project. International Journal of Project Management, 40(6), 608-623. I would love to hear from you if you're interested in complex projects and systems integration. This is an invitation to explore our findings and consider how they might inform your own work. Your feedback and discussions are always welcome! #SystemsIntegration #Infrastructure #Megaprojects #ProjectManagement #Research #OpenAccess #IJPM

The New Face of Risk: When AI Becomes Your Biggest Vulnerability Hook: Artificial Intelligence has become every organization’s favorite ally, and its most underestimated adversary. As enterprises rush to automate, optimize, and predict, they are quietly introducing a new class of risks that traditional frameworks were never designed to handle. Why This Matters AI is no longer a future trend, it’s an operational dependency. From fraud detection to predictive analytics, organizations are embedding machine learning models into their critical workflows. Yet, few are embedding AI governance into their risk programs. The result? A silent explosion of model drift, data bias, hallucinations, privacy exposure, and regulatory uncertainty. In essence, AI has become both the engine of innovation and the epicenter of organizational vulnerability. The Emerging Risk Landscape Here’s how the risk matrix is shifting: Data Integrity Risks: Unverified data sources and uncontrolled training pipelines distort outcomes and decisions. Privacy & Regulatory Risks: Sensitive data fed into AI tools can violate GDPR, HIPAA, and the forthcoming EU AI Act. Operational & Reputational Risks: Unchecked AI outputs can lead to discrimination, misinformation, or reputational collapse. Third-Party & Shadow AI Risks: Employee use of unapproved AI tools leads to hidden data leaks and compliance gaps. Cybersecurity Risks: AI models are becoming targets of prompt injection, model poisoning, and adversarial attacks. The Governance Imperative Mitigating these emerging risks requires structured, proactive AI risk governance ,not reactive compliance. Organizations must: Implement NIST AI RMF or ISO/IEC 23894 frameworks for AI risk management. Establish AI Governance Boards to bridge technical, ethical, and compliance oversight. Integrate continuous model validation to detect bias and performance degradation. Build AI transparency and accountability policies to maintain trust. Embed AI risk indicators into enterprise GRC dashboards for real-time visibility. AI isn’t inherently a risk; the absence of governance is. As the digital economy accelerates, the next major corporate crisis won’t stem from human error, but from machine confidence without human control. “In the age of intelligent systems, risk management is no longer about controlling humans, it’s about governing the minds we’ve built.” @ChiefRiskOfficer @ChiefInformationSecurityOfficer @ChiefDataOfficer @HeadOfCompliance @AI_Ethics_Community @Cybersecurity_Professionals_Network @RiskManagementProfessionals @Governance_Risk_Compliance_Group #AI #RiskManagement #AIGovernance #Cybersecurity #Compliance #DataGovernance #ArtificialIntelligence #GRC #RiskAssessment #TechnologyEthics #ModelRisk #NIST #ISO27001 #AIRegulation #AITrust #BusinessContinuity #OperationalRisk #Leadership #Innovation

✨ Assessing climate risk today secures the rewards of tomorrow. The following report by Tata Consultancy Services shows how climate action shifts from compliance to value creation. It outlines frameworks for embedding climate risk across all business functions. 𝘏𝘦𝘳𝘦 𝘢𝘳𝘦 𝘵𝘩𝘦 𝘴𝘯𝘪𝘱𝘱𝘦𝘵𝘴 𝘧𝘳𝘰𝘮 𝘵𝘩𝘪𝘴 𝘪𝘯𝘴𝘪𝘨𝘩𝘵𝘧𝘶𝘭 𝘱𝘪𝘦𝘤𝘦. 📘 Introduction to Climate Risk Integration: → Integration turns compliance into long-term resilience. → AASB S2 embeds climate risk in decision-making. → Climate risks drive innovation and transformation. → Integration builds transparency and investor confidence. → Collaboration and technology enable effective adaptation. 💼 Value of Integrating Climate Risk: → Strengthens financial resilience and performance. → Builds stakeholder trust through transparency. → Improves access to sustainable finance. → Unlocks new markets and innovation. → Enhances IT systems for better data and reporting. 👥 Leadership and Governance Roles: → Board ensures compliance and strategic oversight. → CEO aligns purpose and resources with climate goals. → CFO links climate risk with financial outcomes. → CRO integrates risks into enterprise frameworks. → CHRO develops skills and climate-linked KPIs. 📜 Frameworks and Regulatory Landscape: → TCFD guides global climate disclosures. → ISSB and IFRS S2 standardize reporting globally. → AASB S2 mandates phased reporting in Australia. → EU and UK lead with strong climate regulations. → TNFD adds biodiversity and nature risk focus. ⚙️ Climate Risk Management Framework: → Embeds climate risk within ERM systems. → Uses scenario analysis for future resilience. → Integrates risk identification, response, and review. → Builds culture of climate awareness and collaboration. → Applies data tools for monitoring and insights. 💡 Business Value Areas: → Strengthens preparedness for climate disruptions. → Enables better, scenario-based decisions. → Reduces compliance and litigation risks. → Boosts investor trust through credible disclosures. → Drives growth via low-carbon opportunities. 🧩 Integration Challenges and Enablers: → Balancing profit with long-term resilience is tough. → Data and literacy gaps slow progress. → Technology and regulation add complexity. → Clear KPIs and governance enable success. → Leadership ensures sustained transformation. 🚀 Way Forward: → Build enterprise-wide climate literacy. → Assess maturity in risk and strategy. → Progress through phased improvement. → Use digital tools for data-driven action. → Collaborate to accelerate low-carbon growth. 😉 With this information at hand, how do you plan to integrate climate risk into your business?