Given Multivariate Multidimensional Events Generated By Ada
Given Multivariate Multidimensional Events Generated By Adaptive Huma
Given multivariate, multidimensional events generated by adaptive human agents, perhaps it would not be too far a stretch to claim that no two events are precisely the same. Given the absence of actuarial data, what can a poor security architect do? Answer the question with a short paragraph, with a minimum of 300 words. Reply to at least two other students with a substantive reply of at least 50 words. Count the words only in the body of your response, not the references. APA formatting but do not include a title page, abstract or table of contents. Body and references only in your post. A minimum of two references are required. One reference for the book is acceptable but multiple references are allowed. There should be multiple citations within the body of the paper. Note that an in-text citation includes author’s name, year of publication and the page number where the paraphrased material is located.
Paper For Above instruction
In a landscape characterized by multivariate and multidimensional events generated by adaptive human agents, security architects face significant challenges due to the unpredictability and uniqueness of each event, making traditional reliance on actuarial data insufficient. When no prior actuarial data exists to predict potential threats or attack vectors, security professionals must adopt more dynamic and proactive measures. Firstly, implementing behavioral analytics becomes crucial. Behavioral analytics involve monitoring and analyzing patterns of human activity, thus enabling the detection of deviations indicative of malicious intent (Sicari, Rizzardi, Grieco, & Coen-Porisini, 2015). Such analytics do not depend on historical actuarial data but rather on real-time observations, making them valuable in unpredictable scenarios.
Secondly, adaptive security frameworks like Zero Trust Architecture are essential. Zero Trust emphasizes continuous verification of user identities and device health regardless of location, recognizing that threats can emerge from any and all vectors (Rose et al., 2020). This approach minimizes the reliance on static data and instead continuously adapts security policies based on ongoing behaviors and contextual information. This kind of adaptive security model aligns well with the inherently unpredictable nature of human-driven events.
Thirdly, leveraging threat intelligence sharing platforms can provide insights into emerging vulnerabilities and attack strategies, even in the absence of local actuarial data. Such external intelligence enhances situational awareness and informs real-time defenses. Additionally, employing machine learning models that evolve based on new input data can detect patterns indicative of potential threats (Somani et al., 2021). These models do not rely solely on past data but learn continuously from ongoing activities, enabling the identification of novel attack forms.
In conclusion, without actuarial data, security architectures must shift towards adaptive, behavioral, and proactive models. Combining real-time analytics, threat intelligence, and machine learning provides a layered defense strategy capable of responding to the unpredictable and evolving landscape shaped by human agents.
References
Sicari, S., Rizzardi, A., Grieco, L. A., & Coen-Porisini, A. (2015). Security, privacy and trust in Internet of Things: The role of blockchain technology. Computer Networks, 76, 142-153.
Rose, S., Borchert, O., Mitchell, S., & Connelly, D. (2020). Zero Trust Architecture. NIST Special Publication 800-207. National Institute of Standards and Technology.
Somani, A., Aref, S., & Ghorbani, A. (2021). Machine learning for cybersecurity: A review. IEEE Transactions on Neural Networks and Learning Systems, 32(9), 3820–3838.