The Governance of Corporate Forensics Using COBIT, NIST and Increased Automated Forensic Approaches
Bibliographic record
Abstract
Today, the ability to investigate internal matters such as policy violations, regulatory compliance, and employee separation has become important in order for corporations to manage risk. The degree of information security threats evolving on a daily basis has increasingly raised concerns for enterprise organizations. These threats include but are not limited to fraud, insider threat and intellectual property (IP) theft. These have increased the demand for organizations to implement corporate forensics as a deterrent to illegitimate acts or for linking perpetrators to their illegitimate acts. This explains why forensic practices are expanding from the traditional role in law enforcement and becoming an essential part of business processes. However, most organizations may not be maximizing the benefits of corporate forensic capabilities because of lack of corporate forensic governance best practices, needed to ensure organizations prepare their operating environment for digital forensic investigation. Corporate forensic governance will help ensure that digital evidence is obtained in an efficient and effective way with minimal interruption to the business. This paper presents a corporate forensic governance framework intended to enhance forensic readiness, governance, and management, and increase the use of automated forensic techniques and in-house forensically sound practices in large organizations that have a need for these practices.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.052 | 0.039 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.006 | 0.003 |
| Science and technology studies | 0.003 | 0.006 |
| Scholarly communication | 0.010 | 0.010 |
| Open science | 0.002 | 0.008 |
| Research integrity | 0.002 | 0.004 |
| Insufficient payload (model declined to judge) | 0.002 | 0.001 |
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 source (direct Gemma or distilled Codex), 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".