13 résultats
pour « risk management »
The main vulnerability in data protection is ineffective risk management, often subjective and superficial. GDPR outlines what to achieve but not how, leading to inconsistent compliance. This paper advocates a quantitative approach for data protection, emphasizing analytics, quantitative risk analysis, and expert opinion calibration to enhance impact assessments.
Cybersecurity investment models often mislead practitioners due to unreliable data, unverified assumptions, and false premises. These models work under idealized conditions rarely seen in real-world settings, so practitioners should carefully adapt them, recognizing their limitations and avoiding strict reliance on their recommendations.
Effective risk management requires understanding aggregate risks, individual business unit riskiness, and systemic risks. Realistic models must consider complex phenomena like heterogeneous marginals and excess kurtosis. A modified individual risk model using Multivariate Stable Distributions addresses these challenges, enabling tractable aggregation, dependence analysis, and Tail Conditional Expectation calculations for aggregate risks.
Organizations rely on complex supply chains, which, while efficient, introduce vulnerabilities. To ensure continuity and align with business strategy, companies must develop robust Supply Risk Management Capability Processes. This process enables proactive identification, assessment, and mitigation of potential disruptions, protecting operational continuity and financial performance. The article offers a detailed guide.
Advancements in AI have reshaped risk analysis, emphasizing scalability, explainability, and simplified reporting. This paper urges the risk field to lead in establishing ethical standards and best practices, calling for research to develop guidelines for emerging applications in risk science."
The paper proposes a novel approach using Monte Carlo Simulation to quantitatively prioritize project risks based on their impact on project duration and cost, addressing limitations of traditional risk matrices and enabling project managers to differentiate critical risks according to their specific impact on time or cost objectives.
The paper suggests that companies developing high-risk AI systems should demonstrate their safety before deployment, arguing for proactive risk management. It proposes a risk management approach where developers must provide evidence that risks are below acceptable thresholds. The paper discusses technical and operational evidence for safety, comparing its approach to the NIST AI Risk Management Framework.
The article explores the importance of critical infrastructure (CI) and essential services (ES) for population security and business continuity. It examines the challenges posed by the interdependence of CI and ES, which complicates threat identification and risk management. The study identifies new research directions on operational risk management, public security, and resilience in critical supply networks.
“… the report underscores the critical role of emerging governance, risk, and compliance frameworks in ensuring organizations remain adaptive and resilient in the face of ever-evolving cyber threats. In an era where digital risks are continuously evolving...”
The book divides into four parts. Part I introduces machine learning in finance, tracing its history. Part II covers practical aspects like model implementation, laden with formulas. Part III details supervised, unsupervised, and reinforcement learning in asset management with case studies. Part IV tackles ethics, regulations, risk, and future trends, aiming for a holistic understanding.