Webscale services dealing with sensitive content are increasingly being deployed in public and hybrid cloud environments. At the same time, the impact of security breaches have also increased manifold averaging at USD 3.86M per data breach. To tackle such increasing risks, regulations and security frameworks are defined that an organization must comply with. Most of these frameworks are published in natural language text that run into hundreds of pages resulting into thousands of requirements and controls. When these frameworks undergo revisions, understanding the changes, and interpreting their impact consumes huge amount of time, effort and resources. In this paper, we propose a change management system that supports SMEs with AI-assisted automation of this extremely manual and time consuming activity. Specifically, we introduce the concept of live crosswalks – a framework that models complex relationships among security and compliance documents along with associated operations to manage the change. It uses natural language processing (NLP) and algorithmic techniques to transform the current document-driven, highly manual process into a data-driven interactive intelligent system. We present the overall design and demonstrate its efficacy over several hundreds of diversified controls through experimental evaluation.

Code-switching has seen growing interest in recent years as an important multilingual NLP phenomenon.Generating code-switched text for data augmentation has been sufficiently well-explored.However, there is no prior work on generating code-switched text with fine-grained control on the degree of codeswitching and the lexical choices used to convey formality.We present COCOA, an encoder-decoder translation model that converts monolingual Hindi text to Hindi-English code-switched text with both encoder-side and decoder-side interventions to achieve finegrained controllable generation.COCOA can be invoked at test-time to synthesize codeswitched text that is simultaneously faithful to syntactic and lexical attributes relevant to code-switching.COCOA outputs were subjected to rigorous subjective and objective evaluations.Human evaluations establish that our outputs are of superior quality while being faithful to desired attributes.We show significantly improved BLEU scores when compared with human-generated code-switched references.Compared to competitive baselines, we show 10% reduction in perplexity on a language modeling task and also demonstrate clear improvements on a downstream code-switched sentiment analysis task.