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GAIL · LESSON 4

Section 4: Business strategies for a successful generative AI solution約15% · 3 topics

Learn how to approach generative AI adoption, secure AI, and responsible AI.

4.1Approaching generative AI adoption

Approaching generative AI adoption (solution types, factors affecting needs, choosing the right solution, steps to integrate into the organization, measuring impact)

Concept

Choose a solution type (ready-made product, customization, custom development) based on business needs, data, technical skills, budget and timeline. Integrate into the organization in the order set goals → try small → measure impact → roll out and embed (training, change management). Measure impact with KPIs such as productivity, quality, customer satisfaction and cost.

How it works / steps

  1. Decide the business problem to solve and the KPIs
  2. First check whether a ready-made product is enough
  3. Measure impact in a pilot and roll out if it succeeds
  4. Embed it with training and internal champions

Workplace example

Before a company-wide rollout, run a 2-month pilot with 50 people in sales, measure the reduction in proposal-writing time, then decide on rollout.

Common confusions and exam tips

Mini quiz

Q1. What is the most appropriate way to prioritize generative AI use cases?

  1. Start with ones using technology executives have heard of
  2. Evaluate by size of impact and ease of delivery, and choose
  3. Always start with the hardest, unprecedented ones
  4. Start with ones whose effect can't be measured
Show answer and explanation

Answer: B Evaluate by size of impact and ease of delivery, and choose

Early results (quick wins) build organizational understanding and support.

Q2. You rolled out a gen AI tool company-wide, but usage is still low after three months. Which organizational action is most appropriate?

  1. Show executive sponsorship, and provide department use cases, training and usage rules
  2. Switch to a higher plan with more features to encourage use
  3. Concentrate budget on a few heavy-use departments and stop rollout elsewhere
  4. Make usage rates a department KPI and require use in departments that miss targets
Show answer and explanation

Answer: A Show executive sponsorship, and provide department use cases, training and usage rules

Change in people and processes is needed, not just technology. [Other options] More features don't help if people don't know how to use them. Mandates produce token use and unmeasurable effects. Stopping rollout skips analyzing and fixing the cause.

Reference links

4.2Secure AI

Secure AI (security across the ML lifecycle, SAIF, secure-by-design infrastructure, IAM, Security Command Center, workload monitoring)

Concept

Think about AI security across the whole ML lifecycle (data, training, deployment, operation). Google's SAIF (Secure AI Framework) is a framework for protecting AI systems. Combine a secure-by-design foundation, least privilege with IAM, understanding risks and threats with Security Command Center, and workload monitoring.

How it works / steps

  1. Identify risks at each stage: data, model and app
  2. Give least-privilege access to AI resources with IAM
  3. Get a central view of risks and threats with Security Command Center
  4. Counter AI-specific threats such as prompt attacks

Workplace example

When introducing an internal agent, assess risks from the SAIF viewpoint and limit the agent's service account to reading only the data it needs.

Common confusions and exam tips

Mini quiz

Q1. You plan to tune a model on internal data and publish it as an agent. Which combination best covers security across the whole ML lifecycle?

  1. Filtering user input, checking output and applying security patches
  2. Encrypting training data, managing permissions of training job runners, and model versioning
  3. Stronger authentication for the published endpoint, encrypted traffic and stored access logs
  4. Preventing training data poisoning, least privilege for the training environment, and prompt injection defenses after release
Show answer and explanation

Answer: D Preventing training data poisoning, least privilege for the training environment, and prompt injection defenses after release

Risks differ at each stage. [Other options] Each other combination is useful but covers only release, only training, or only operations. Lifecycle security protects data, training and post-release stages.

Q2. What is the most appropriate purpose of SAIF (Secure AI Framework)?

  1. Showing how to deliver AI ads effectively
  2. Showing contract methods to minimize AI fees
  3. Setting out principles for designing and operating AI systems securely
  4. Showing training methods to maximize AI model accuracy
Show answer and explanation

Answer: C Setting out principles for designing and operating AI systems securely

SAIF includes elements such as extending strong security foundations to AI, extending detection and response, and automating defenses.

Reference links

4.3Responsible AI

Responsible AI (transparency, privacy, anonymization and pseudonymization, data quality, bias and fairness, accountability and explainability)

Concept

Responsible AI emphasizes transparency (explaining that and how AI is used), privacy (protecting data through anonymization, pseudonymization and the like), data quality, bias and fairness, and accountability and explainability. Google's AI Principles include being socially beneficial, avoiding unfair bias, safety, accountability and privacy.

How it works / steps

  1. Tell users that AI is used and its limits
  2. Anonymize or pseudonymize personal data before use
  3. Check training data and output for bias
  4. Set up a way to explain the reasons for decisions, and an owner

Workplace example

When using AI to summarize job applications, pseudonymize applicants' names and gender, check for differences in summary quality by attribute, and leave the final decision to people.

Common confusions and exam tips

Mini quiz

Q1. You explain “secure AI” to executives. Which explanation is most appropriate?

  1. Using AI to detect network threats and automate responses to cyberattacks
  2. Ensuring fairness and transparency so AI output isn't harmful and earns public trust
  3. Protecting data, models, apps and infrastructure from attack and misuse across the whole lifecycle
  4. Scanning models for vulnerabilities before release and deploying only those that pass
Show answer and explanation

Answer: C Protecting data, models, apps and infrastructure from attack and misuse across the whole lifecycle

Protect all of data, models, apps and infrastructure. [Other options] The first is responsible AI, the second is using AI for security, and the third covers only one pre-release step.

Q2. Which is the best example of a privacy risk in generative AI?

  1. Personal data entered is unintentionally stored, shared, or appears in output
  2. Privacy risks only occur in image generation
  3. Generative AI can't handle personal data at all, so there's no risk
  4. Putting personal data in prompts always raises model accuracy
Show answer and explanation

Answer: A Personal data entered is unintentionally stored, shared, or appears in output

Data minimization, anonymization/pseudonymization, access control, and checking how data is handled.

Reference links

Lessons are written by this site based on public information and do not guarantee exam content. Product specifications change, so check the latest information at the reference links.

Notes (unofficial, original questions, no pass guarantee)