Instructor: In this lesson, we will learn about AI bots,
artificial intelligence, or AI bots,
our automated software programs
that interact with users or systems
to perform specific tasks.
They may also serve as assistants
and digital workers.
AI bot concepts include access and permissions,
guardrails, data loss prevention, or DLP,
and disclosure of AI usage.
Access and permissions refer to the controls put in place
to ensure that AI bots only interact
with data and systems
that they are explicitly authorized to access.
By doing this, access and permissions
prevent unauthorized data manipulation
and security breaches.
Next, guardrails are predefined boundaries or rules
that restrict the actions of AI bots.
Then data loss prevention or DLP strategies
are used with AI bots
to prevent sensitive information
from being accessed or leaked inadvertently.
Finally, disclosure of AI usage
involves informing users
that they are interacting with an AI bot
rather than a human.
Let's learn more about access permissions,
guardrails, data loss prevention,
and the disclosure of AI usage.
First, we have access and permissions.
Access and permissions control what an AI bot can see
or do within a system.
So access requires setting explicit limits
to make sure bots access only the data and systems
they need to complete their tasks.
Permissions then act as a safety barrier,
ensuring AI bots can't overreach
into sensitive areas.
For example, in a healthcare setting,
a patient assistance bot
may only need access to general medical information
to answer common questions,
not specific patient records.
So by carefully setting these permissions,
the bot stays within its lane,
interacting with data safely
without risking unauthorized access
to patient details.
In industries where AI bots
work closely with financial data,
such as in the banking industry,
access controls can be even more important.
For example, an AI bot helping customers
with account information
would need tightly controlled access
to prevent it from making unauthorized transactions
or accessing sensitive financial records
without permission,
minimizing the risk of security breaches
or data manipulation.
So to prevent unauthorized access
or misuse of sensitive information,
access and permissions keep AI bots
securely within their defined roles,
protecting both data integrity and user privacy.
Second, we have guardrails.
Guardrails are boundaries set up around AI bots
to keep them operating within intended limits.
These rules help guide the bot's actions,
ensuring they don't veer into unintended territory
or make autonomous decisions that could be harmful.
For example, in an online shopping application,
a product recommendation bot
might have guardrails
that prevent it from suggesting unavailable
or out-of-stock products.
These guardrails help the bot provide accurate
and reliable information to the users,
preventing misunderstandings or dissatisfaction.
In a business context,
consider a cleaning bot
in a Sam's Superstore,
driving around and cleaning the floors.
Guardrails are set to prevent this bot
from colliding with customers or merchandise,
ensuring it navigates safely around the store.
These guardrails define specific paths
or areas for cleaning,
restrict its movements around crowded aisles,
and ensure it stops or reroutes
when encountering obstacles.
By establishing these boundaries,
the store guarantees that the bot performs
its cleaning duties efficiently
without compromising customer safety
or risking damage to merchandise.
So by having predefined rules in place,
companies ensure their bots remain helpful
and don't step into areas
they are not programmed to handle,
maintaining quality and safety in their interactions.
Third, we have data loss prevention, or DLP.
Data loss prevention measures are deployed with AI bots
that handle sensitive information
to prevent unintended exposure of sensitive data.
DLP strategies,
like encryption or restricted access policies,
ensure that sensitive information
stays within the authorized limits
and is protected from unauthorized exposure.
For instance, a banking chatbot
might be designed with DLP measures
to restrict any sharing
of a user's financial details
without proper verification.
This helps in safeguarding user data
and building trust in the bot's usage
with insensitive environments.
In a corporate environment,
an HR department might use an AI bot
to assist employees with common inquiries
about company policies,
benefits, or leave balances.
DLP measures would be employed
to ensure the bot cannot access
or share personal information,
like Social Security numbers,
salary details, or medical leave records,
unless the employee has gone through identity verification.
This helps prevent sensitive human resources data
from being disclosed inadvertently
or to unauthorized users.
So to prevent accidental leaks
or unauthorized access to sensitive information,
DLP strategies ensure that AI bots
operate within strict boundaries,
keeping data secure and interaction safe.
Fourth and last, we have disclosure of AI usage.
Disclosure of AI usage involves letting users know
they are interacting with an AI bot
rather than a human.
This is essential for transparency and trust.
Disclosure of AI usage
allows users to understand
the nature of their interaction
and set appropriate expectations.
For instance, a customer service chatbot
on a retail website
could display a message at the start of a conversation
stating, "I am an AI assistant,
here to help you with product inquiries."
Such clear disclosure builds transparency
and makes users more comfortable
interacting with the bot.
In healthcare settings,
disclosure is especially important when using AI bots
for patient interactions.
For instance, if a healthcare bot
provides a general wellness advice
or answers to common medical questions,
patients should be clearly informed
that they are speaking to an AI,
not a licensed medical professional.
This helps set accurate expectations
as the bot may lack the depth of expertise
or personal insight
that a doctor would provide.
So by disclosing AI usage,
businesses help users approach the bot's responses
with the right level of caution and understanding,
supporting trust and clarity
in the service experience.
So, remember, artificial intelligence or AI bots
are automated programs
designed to perform specific tasks,
often simulating human interactions.
They are commonly used as digital assistants
or workers in various settings,
including customer service and data management.
To work securely,
AI bots are set up with access and permissions,
which control what data and systems
they can interact with,
helping prevent unauthorized access.
Next, guardrails define safe boundaries,
guiding bots to stay within their designated tasks.
Then data loss prevention strategies
protect sensitive information
from being accidentally accessed
or leaked by the bot.
And finally, disclosing AI usage to users
promotes transparency and builds trust
by letting them know they're interacting with a bot
rather than a human.