A Holistic Approach to Information Security Assurance and Risk Management in an Enterprise
Bibliographic record
Abstract
The hypergrowth of computing and communications technologies increases security vulnerabilities to organizations. The lack of resources training, the complexity of new technologies, and the slow legislation process to deter the breach of security all constitute to the trends of increasing security risk in an enterprise. Traditional approaches to risk assessment focusing on either the departmental or branch level lacks of an enterprise perspective. Many organizations assess and mitigate security risks from a technology perspective and deploy technology solutions. This approach ignores the importance of assessing security risk in policy and execution. This chapter discusses a systematic and holistic approach to managing security risk. An approach that utilizes the information life cycle and information assurance (IA) assessment points for the creation of policy, monitoring, auditing of security performance, regulate, and initiate corrective action to minimize vulnerabilities. An “information life cycle” is being proposed with its stage value and the underlying security operatives (gate-points) to protect the information. An information assurance framework and its functions to audit the information security implemented in an enterprise are proposed. Organization must assess the value and the business impact of the information, so that optimal and effective security system and security assurance can be designed.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one teacher head, not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".