Corporate auditors should already be addressing CMMC.

https://www.fifthdomain.com/dod/2020/03/09/the-pentagons-first-class-of-cybersecurity-auditors-is-almost-here/

The Pentagon’s first class of cybersecurity auditors is almost here

The Pentagon hopes to have the first class of auditors to evaluate contractors’ cybersecurity ready by April, a top Department of Defense official said March 5.

The auditors will be responsible for certifying companies under the new Cybersecurity Maturity Model Certification (CMMC), which is a tiered cybersecurity framework that grades companies on a scale of one to five. A score of one designates basic hygiene and a five represents advanced hygiene.

Cases, just in case. (Free PDF)

https://papers.ssrn.com/sol3/papers.cfm?abstract_id=3547103

Cybersecurity Law, Policy, and Institutions (version 3.0)

This is the full text of my interdisciplinary “eCasebook” designed from the ground up to reflect the intertwined nature of the legal and policy questions associated with cybersecurity. My aim is to help the reader understand the nature and functions of the various government and private-sector actors associated with cybersecurity in the United States, the policy goals they pursue, the issues and challenges they face, and the legal environment in which all of this takes place. It is designed to be accessible for beginners from any disciplinary background, yet useful to experienced audiences too.

The first part of the book focuses on the “defensive” perspective (meaning that we will assume an overarching policy goal of minimizing unauthorized access to or disruption of computer systems). The second part focuses on the “offensive” perspective (meaning that there are contexts in which unauthorized access or disruption might actually be desirable as a matter of policy).

Another perspective.

https://www.justsecurity.org/69054/an-ambitious-reading-of-facebooks-content-regulation-white-paper/

An Ambitious Reading of Facebook’s Content Regulation White Paper

Corporate pronouncements are usually anodyne. And at first glance one might think the same of Facebook’s recent white paper, authored by Monika Bickert, who manages the company’s content policies, offering up some perspectives on the emerging debate around governmental regulation of platforms’ content moderation systems. After all, by the paper’s own terms it’s simply offering up some questions to consider rather than concrete suggestions for resolving debates around platforms’ treatment of such things as anti-vax narratives, coordinated harassment, and political disinformation. But a careful read shows it to be a helpful document, both as a reflection of the contentious present moment around online speech, and because it takes seriously some options for “content governance” that – if pursued fully – would represent a moonshot for platform accountability premised on the partial but substantial, and long-term, devolution of Facebook’s policymaking authority.

For my Architecture students.

https://www.forbes.com/sites/cognitiveworld/2020/03/08/the-emergence-of-ml-ops/#36b201d44698

The Emergence Of ML Ops

In the latter part of the 2000s, DevOps solutions emerged as a set of practices and solutions that combines development-oriented activities (Dev) with IT operations (Ops) in order to accelerate the development cycle while maintaining efficiency in delivery and predictable, high levels of quality. The core principles of DevOps include an Agile approach to software development, with iterative, continuous, and collaborative cycles, combined with automation and self-service concepts. Best-in-class DevOps tools provide self-service configuration, automated provisioning, continuous build and integration of solutions, automated release management, and incremental testing.

… DevOps approaches to machine learning (ML) and AI are limited by the fact that machine learning models differ from traditional application development in many ways. For one, ML models are highly dependent on data: training data, test data, validation data, and of course, the real-world data used in inferencing. Simply building a model and pushing it to operation is not sufficient to guarantee performance. DevOps approaches for ML also treat models as “code” which makes them somewhat blind to issues that are strictly data-based, in particular the management of training data, the need for re-training of models, and concerns of model transparency and explainability.

As organizations move their AI projects out of the lab and into production across multiple business units and functions, the processes by which models are created, operationalized, managed, governed, and versioned need to be made as reliable and predictable as the processes by which traditional application development is managed.

Yesterday I was the most popular Blog in Turkmenistan. I have no idea why.