System: You are provided with a paragraph. Your goal is to automatically identify the most relevant tasks based on the content of the paragraph and generate an example of entailment (if the task conclusion can be logically inferred from the paragraph) and a different example of non-entailment (if it is unclear or the opposite conclusion is true). 

Step 1: Read the paragraph carefully. 
Step 2: Automatically decide which tasks are relevant based on the paragraph’s content. Relevant tasks may include, but are not limited to: 
    - Topic Classification 
    - Risk Classification
    - Sentiment Classification 
    - Tone Classification
    - Event Detection 
    - Entity Recognition 
    - Geographical Recognition 
    - Trend Detection 
    - Intent Detection 
    - Sector Identification 
    - Impact Classification 
    - Tense Identification (Past, Present, Future) 
    - Uncertainty Identification 

Step 3: For each relevant task you identify, generate two clear and concise examples whose labels match common practices for the task at hand: one for entailment and one for non-entailment. The example should speak about the text at the 3rd person. 
Step 4: Output the results in the following JSON format:

{
    'relevant_tasks': [
        {
            'task': <The automatically selected relevant task>,
            'examples': [
                {
                    'input': <The entailment example based on the paragraph>,
                    'label': 'entailment',
                },
                {
                    'input': <The non-entailment example (neutral or contradiction) based on the paragraph>,
                    'label': 'non-entailment',
                }
            ]
        },
        ...
    ]
}
User: In the ordinary course of our business, we collect and store sensitive data, including intellectual property, our proprietary business information and that of our customers, suppliers and business partners, and personally identifiable information of our customers and employees, in our data center and on our networks. The secure processing, maintenance and transmission of this information is critical to our operations and business strategy. In response to the COVID-19 pandemic, our move to a work-from-home business model may heighten risks of security breaches. Despite having implemented security measures to address risks of security breaches, we experienced a cyber-security breach during the third quarter of 2022 involving a single employee email account and which indirectly impacted two Mastech InfoTrellis clients. We accrued a pre-tax loss reserve of $450,000 in the third quarter 2022 related to this event, which reserve includes the cost of engaging external advisors and an estimate of other potential losses relating to the breach. While we adopted certain remedial measures as a result of this incident, our information technology and infrastructure may still be vulnerable to security breaches and other disruptions, including attacks by hackers, or breaches due to employee error, malfeasance or other disruptions. Any such breach or disruption could compromise our networks and the information stored there could be accessed, publicly disclosed, lost or stolen. Any such access, disclosure or other loss of information could result in legal claims or proceedings, liability under laws that protect the privacy of personal information, and regulatory penalties, disrupt our operations and the services we provide to customers, and damage our reputation, and cause a loss of confidence in our services, which could adversely affect our operating results and competitive position. We maintain cyber risk insurance, but this insurance may not be sufficient to cover all of our losses from any breaches of our networks
Assistant: