GDPR Compliance- Using Machine Learning to Enhance Privacy

In the current global tech market, many companies have their data scattered across multiple networks, for a lot of business (personal) data processing is a significant activity. “Personal data”- it might be any information relating to an individual’s private, professional, or public life which is used, shared and transferred around the globe instantaneously, making it difficult for people to secure their personal information. Even governments also have a security interest in ensuring the protection of personal data as they can see breaching of highly sensitive personal data. Now it’s the time for such organizations have likely identified the process changes they need to ensure compliance. if not, you better hurry

GDPR is the recent reminder compliance comes with a high level of awareness and a comprehensive data management strategy. GDPR is applicable to all members of the EU and EEA from May 25, 2018, and to all companies handling the consumer data of citizens within the European Union (EU), no matter the size, industry or country of origin of the business. Also keep in mind If a firm infringes on provisions of the GDPR it can be penalized up to €10 million, or 2% of the worldwide annual revenue of the prior financial year for the lower level and in the upper level Up to €20 million, or 4% of the worldwide annual revenue of the prior financial year.

Ever wondered! is there any “solution for sensitive data protection?” Adapting to GDPR compliance? also enhancing security…? Ai with ML serves the right purpose by solving cybersecurity problems using computerized analytical processes adhering to GDPR compliance. As the market for enhanced security and privacy grows.

Here are the key roles of machine learning in enhancing data privacy
• Machine learning-driven solutions will provide services without exposing PII (personally identifiable information) that can lead to misuse a person’s identity, such as the name and the surname, or the IP address in combination with the physical address
• Machine Learning models can effectively analyze huge data sets in real time to detect specific patterns, anomalies, and trends. Analyses every single packet where all the sensitive information and potential, PII is being stored
• Machine Learning works well with GDPR’s, uses algorithms to analyze patterns in data, therefore minimizing the need for human supervision and PII exposure
Here are the examples relates and showcases how Machine learning helps in meeting GDPR compliance
• AI can help understand the GDPR and any update or act stemming subsequent or regulation from it. NLP (Natural Language Processing) solutions can read an interpret regulations and analyze actually what the document is about and what changes to the law affect privacy and who are the stakeholders involved, etc. Furthermore, NPL combined with ML can upgrade the game because the latter one requires machine-executable functions.
• Data breaches and fraud becoming a serious issue, can be controlled by AI-powered security analytics. Advancing quickly and representing an unmissable opportunity to improve cybersecurity. In particular, ML can detect advance threats (detects behavioral changes), and also eliminate a large number of manual tasks.

Machine learning will be the most viable solution to the current issues of security, privacy and end-user protection and more than that people are becoming more aware on the risks that being online brings. So, where is your business in the GDPR compliance game? Our idea is to make businesses and governments more accountable for their data and the way they use it. Get to know more about our services in supporting GDPR compliance Reach us….

Together, we can build a secured machine learning solution for the right to protection of personal data.

Extending Five Factor model to Chatbot Personality

https://www.intellectyx.com/blog/chatbot-personality/

When it comes to human personalities, I relate to the five personality traits theory aka five factor model (FFM). I will not go too detail into this as this blog is more focused on how this model can be extended to defining/designing chatbot personality. The Five factor model defines the taxonomy for personality traitsby using the below five factors as listed below –

• Openness to experience
  – defined by the degree of curiosity, creativity and preference for novelty and variety involved. It is also described as the extent to which a person is imaginative or independent and depicts a personal preference for a variety of activities over a strict routine.

• Conscientiousness
  – defined by the degree of efficient and organized. This is usually reflected by the planned vs spontaneous behaviors. In other words, low conscientiousness means more flexibility and spontaneity but can also appear low reliability.

• Extraversion
  – defined by the degree of outgoing and energetic. This is usually reflected by more positive emotions, assertiveness and sociability.

• Agreeableness
  – defined by the degree of friendly and compassionate.This is usually reflected by the measure of trust, helpful and even the level of submissiveness. Low agreeableness also seen as most often competitive/challenging and as argumentative.

• Neuroticism
  – defined by the degree of sensitiveness, emotional stability and impulse control. Higher neuroticism is viewed as more unstable, and insecure.

Phew…now that we have covered the traits, let’s see how we can relate these traits to chatbot personality or chatbot persona by using the dialog and intent

Factors	Intent or Dialogs that could be used to identify chatbot personality	Example chatbot personality BOTs
Openness to experience	This trait could be defined by how focused the BOT on a particular subject is – is it more generalist or specialist BOT?	I see generalist BOTS like Siri, Alexa are more towards high openness to experience vs. specialty BOTS such as task focused – pizza, weather, news bots etc., If Iam creating a Pizza ordering Chabot, I would want it anyhow to ne more task focused vs. openness to experience.
Conscientiousness	This trait could be defined by the approach of how the conversations are conducted – is it more open ended vs. menu driven? More the planned, the more the conversation is orchestrated via menus. More menu driven bots in my opinion could be characterized as high in conscientiousness. Dialogs such as below would relate to more high conscientiousness bots – “Please pick one of the options listed below.” “I don’t understand, Can you pick one of the below”	E.g. Food ordering chatbots where conversations are very focused and can be conducted via menu driven vs. sales process driven chatbots where its open ended.
Extraversion	The way the information is collected – actively or passively. Some bots tend to collect first the information even before proceeding with the business process. The other aspect is not following a predicted path and more outgoing in the responses and opening up conversations such as ““I see you are from LA…What is the weather like today?”	E.g. Sales bot would be more extravert where it can be quite adamant that it needs the information first before any further information could be sent.
Agreeableness	Use of words that share emotions and showing a state of agreeableness in responses. Dialogs such as “I agree…” “Yes, I understand…” “Sorry that you are facing…”	Customer support BOTS should rank higher on trust, helpfulness and empathize more with the customers without any judgements.
Neuroticism	The stable the platform is. The more stable, the less neurotic. I see there are lots of bots which is not well trained or doesn’t have all flows covered or very rudimentary. All these bots would fall under highly neurotic	More mature bots which are very consumer focused with voice assisted services like Alexa, Siri that has been trained over many consumers would fit the profile of low neurotic.

This is my effort towards encouraging users to have a good design and planning around any bot development before deep diving into development. 80% Planning and 20% development is how it should be.