Summarise the following into one paragraph: 
Risks associated with data. The first big risk with the data is that there could be potential delays in pulling anonymized data that is ready for analysis from the current infrastructure. Additionally, the data we might collect is that those may be largely unlabeled, e.g., without clear identification of regular or anomalous events. We may detect things that were not detected before by simple thresholding rules. Lastly, one potential challenge associated with the data is missingness, and determining whether missingness should be part of the monitoring procedure. To address these challenges, and mitigate the risks, we will work closely with ISS to have immediate feedback about the data. This should help to minimize delays and ensure that the analysis can proceed smoothly. To this end, we have factored in the project costs enough time for technical support from ISS to help. 
Risks associated with the analysis. Multivariate analysis comes with a well-known set of statistical challenges. In an anomaly detection setting, we might find ourselves in a situation where the majority of the variables tracked are non-anomalous or missing. This can pose a challenge for statistical analysis as it can make it difficult to identify anomalous patterns amongst regular behaviors. To this end, we expect much of the work to be with in identification and exclusion of variables that are not significant to the analysis. Additionally, we could mitigate the issue by carefully selecting and aggregating variables.  
The other major issue might come with having a method too sensible, that would result in many false positives over expected behaviors, and those could severely impair the analysis. This might come from the fact that the multiple variables observed may be extremely heterogeneous: for example, differences in behavior between night and day may severely affect detection performance. Issues such this one can be addressed by integrating covariates to standardize or normalize the data or by running separate analyses for different time frames. 
To minimize this risk, we allow plenty of time for careful tuning of the methodology, taking advantage of super mentioned techniques such as the TS algorithm. 
Technical risks. There are many risks associated with integrating the procedure in the current monitoring infrastructure. One of the major ones is that the behavior observed in the offline analysis does not scale well with the existing infrastructure, negatively impacting the performance of the ex Write your response with exactly 7 paragraphs, separated with the markdown divider: ***.
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