Saturday, February 15, 2025

Security Practices Questions

It would be fun to record some of my answers to my cybersecurity course. 

Propose a NIST-style recommendation for passwords.

A recommendation for an enterprise is to implement the concept of passphrases followed by MFA as a requirement instead of making the user do gymnastics to come up with a complex combination of characters with uppercase, lowercase, and special symbols when creating a password. Another policy recommendation would be to allow or mandate a password reset if the enterprise experiences a breach or an individual's credentials are compromised. The move toward passwordless authentication models that the FIDO alliance proposes would be another recommendation for using biometric and hardware tokens. The advantages of these ideas are that they increase security best practices while moving & balancing user experience to comfort and security hygiene while avoiding frustration for users.

What does it mean if a website "leaks" information? What sort of information might a (poorly designed) website actually leak in practice?

A website that is not configured correctly in the backend unintentionally exposes the config file or shows unsecured log files, which can be exploited to sniff out user creds or allow threat actors to create SQL injection attacks where a poor DB design or exposed database architecture is some examples of a website leaking information. The website may also expose phone numbers and emails, allowing for social engineering attacks by threat actors. A website or a web app that uses unprotected or old APIs that are poorly configured can expose tokens or output JSON dump files that can allow for data theft and control of information. Administrative credentials can also be compromised for poorly designed websites, exposing user credentials in text and log files.


Suppose your boss asks you whether the company should use single sign-on (SSO) for the company's web app. Explain in a short paragraph why you might want to use SSO. But also explain why you might not want to use SSO.

An enterprise today, be it small, medium, or big, uses various types of applications in its environment. Identity management is crucial for protecting our employees and the organization’s

Image created by Dall-E

digital assets and helping employees increase security, adhere to policy, and prevent password fatigue. These are the primary advantages that I propose for using and enforcing a single sign-on (SSO) in the enterprise. Access management and layering security are other advantages. An argument against SSO would be that if introduced as the single point of entry for identity management, it creates a high risk of also being the single point of failure. Costs and licensing issues on how you go about it can impact your operational expense budgets. I saw this first hand at the global non-profit I served, where securing 1500 licenses cost increases per user with the identity management solution provider we used. Another disadvantage was complexity increased with integrating SSO with legacy software that was not designed to be served up for access that way, and the need to re-engineer or rebuild those apps pushed the overall costs up.


Suppose your boss's boss asks you whether the company should require password managers for all employees. In a short paragraph, explain why you might want everyone to use a password manager. But also explain why you might not want everyone to use one.

A password manager is a must in an age where individuals and organizations have a ton of passwords to store, keep, and use to access their apps and systems to get work done. Remembering all these passwords, reducing the risk of using weak passwords, reducing the risk of reusing passwords, and going through the gymnastics of making complex passwords that no one can remember are some challenges that password managers can easily overcome. Protocols for removing access when employees are fired, or transition and management of what systems they had access to, and features like breaking the glass for super admins who may leave are some features that password managers provide that are super useful. An argument against everyone using password managers could be creating a single pane of reliance, meaning if the vendor is attacked, it could compromise all your credentials. Many vendors provide individual accounts to be connected with corporate accounts, and the creation of policies and cross-contamination of passwords or password theft could happen. Employees have to be trusted with critical accounts, and a backup escalation policy may not be in place, which could create potential threats for the enterprise and, of course, the ongoing rise in costs when the password manager vendor raises the price per license.

Play The Password Game -- try to get through at least Rule 12.

While obviously, the game itself is, in many ways, meant to be humorous, it also critiques the experience many of us have when setting up new passwords. Explain in a short paragraph, at most, how there's a trade-off between usability and security in the context of passwords.

Easy passwords are easy to remember and more straightforward to set up but are vulnerable to attack, while very complex passwords are hard to set up and remember. The trade-off between usability and high security is always up for debate, frustration, and user experience disruption/acceptance in accessing and using applications. A password policy and programming the user log-in interface approach that allows for a balanced approach that considers user experience, password hygiene, and high security is a tricky balance to achieve. Still, it needs to be pursued during heightened fraud and targeted attacks. As a software product architect, I strive and advocate for balancing security and usability to ensure a smooth user experience and awareness that we are taking security seriously and winning customers with delight. However, every use case and environment is different, and trade-offs have to be considered. A military application that works with nukes or scrambling fighter jet codes differs significantly from an entertainment game app on the mobile phone and requires appropriate evaluating and balancing of these trades.


Authenticated vs. Authorized

Grading comment:

In no more than three sentences, what does it mean to be authenticated but not authorized to access some web page?


Authentication means your identity checks out with the app or system, your access to it is validated and verified, and you have the key to get in. However, Authorization to the system corresponds to permissions, roles, and attributes to access a resource, page, or asset. A user who is not authorized means you may be a valid user but do not have the necessary role, privileges, permissions, or attributes to access content or assets or perform specific actions.

Inherence Factor

Identify something that would typically be considered an inherence factor.


During authentication, an inherence factor is usually attributed to something the user inherently possesses, like a fingerprint, iris scan, voice, or facial recognition that is unique to the person. These biometric authentication, like fingerprint access on our Mac laptops or phones or facial scans in Windows 11, are authentication methodologies that add layers and make it harder for threat actors to replicate or break. We would consider inherence factors in authentication to provide a higher level of security. Inherence is intrinsic, unique, and associated with the individual. It is also harder to replicate. Thirdly, my favorite is that it's far more convenient to use my fingerprint or look at my computer than to remember a password.


2FA vs 2SA

In no more than a short paragraph, distinguish between two- (or multi-)factor authentication and two- (or multi-)step authentication.


MFA or 2FA requires multiple authentication methods that consider knowledge, which is something you know, like your password, inherence like a biometric fingerprint or facial scan, and something you have, like a Yubico USB or a hardware token. A two-step authentication (2SA) is similar, but it could be the user's password and a code generated that you must validate via email or text. Though both methods enhance security, MFA is stronger as a user can change and provide layers of defense that are harder to replicate for threat actors/attackers.

Sunday, February 9, 2025

Random Thoughts - Lessons from 'Don't say UM'

 

Among all the podcast cycles I listen to, Brad McKay’s “The Art of Manliness” is one of my favorites. Brad is skilled in many ways, but his approach to questioning the experts he invites on the show provides practical insights you can implement in your ongoing journey to become a better man. For instance, in one of his latest podcasts featuring Michael Chad Hoeppner, the author of “Stop Saying ‘Um,’” I gleaned some valuable nuggets that I want to share here.

 

Michael discusses the concept of creating a virtuous cycle. To illustrate this, he compares looking through binoculars, where your left hand resembles the letter ‘C,’ and your outer right hand appears as a capital ‘D.’ He suggests talking as a blend of content and delivery, where joining your hands forms a reinforcing loop. Pausing to breathe, embracing silence, and enhancing your voice’s vocal variety by expressing your content’s emotions through precise enunciations can significantly improve your delivery. When your delivery is strong, you sound better and project confidence and empathy, boosting your self-belief.

 

Michael argues that talking is a physical act, much like any sport or physical activity. It takes about 100 muscles to perform this miracle that we often take for granted. The more intentionally we warm up and practice, the better we become at delivery, whether in first meetings, on dates or when speaking to teams or large crowds. One quote that resonated with me was when he said: “Talking is just a series of decisions. It is a flowchart of words where your brain performs the miracle of choosing one word after another and assembling them into a system that can be meaningful, powerful, and persuasive to others. So embrace that decision-making process you engage in all day, and don’t shy away.”

 

In his book, Michael presents practical exercises, such as using a Lego during your delivery to help you tolerate silence, be concise, and eliminate fillers like ‘um’ or ‘does that make sense’ (something I often do from my teaching/professor days). Instead of rushing through your ideas, slow down before picking up the


next Lego brick. With our world shifting to audio communication through Zoom meetings, he suggests another exercise: walking your fingers across the table while delivering your idea, pausing, and then walking them back. These practices can prevent you from rambling and help create meaningful conversations.

 

Michael also emphasizes how focusing on enunciation can benefit us by encouraging a slower pace. This idea is not new; it dates back to ancient Greece. The orator Demosthenes practiced by placing round pebbles in his mouth, a technique noted by the historian Herodotus. You can replicate this by using a wine cork or a toothbrush on the side of your mouth, ensuring every syllable is clear and pronounced, even with the impediment in your mouth.

 

Another tool that actors and voice actors use is tongue twisters. I used to enjoy them in high school without realizing that voice specialists utilize tongue twisters as warm-up exercises, like the examples below.

 

Making eye contact, reading cues, and gathering feedback are crucial in conversations and when addressing teams or crowds. Observe individuals in the audience and try to elicit nonverbal cues to determine whether your message resonates. Shift your focus to different people throughout the room. In large audiences, making eye contact with everyone is unnecessary—if you’re speaking to thousands, that would be impossible. However, your communication naturally strengthens by connecting with individuals in various sections. Eye contact enhances engagement and makes your message more impactful.

Thursday, February 6, 2025

Three AI Frameworks adoption model for Organizations

 

Leaders are scrambling today to somehow integrate AI into their organizations. Many companies and institutions who are at the forefront of this have written some whitepapers, and I have picked 3 AI Usage Frameworks that I liked for developing your own Organizational AI Adoption Plan. A good plan, like any, should bring cross-functional leadership to the table and discuss a model and a plan before driving the implementers or engineers to adopt it because everybody else is doing it. 

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Microsoft’s AI Maturity Model proposes the stages of AI adoption in organizations and how human involvement changes at each stage:

 Source: https://devblogs.microsoft.com/azuregov/fed-agencies-exec-order-ai-part1/
  • Provide a framework for Federal agencies to shift from Cultural Shift to Ownership, recognizing governance, innovation, risks, and compliance as key pillars. 
  • Assisted Intelligence: AI provides insights, but humans make decisions.
  • Augmented Intelligence: AI enhances human decision-making and creativity.Mic
  • Autonomous Intelligence: AI makes decisions without human involvement.                                                      

HBR's Human-AI Teaming Model outlines a framework for this type of collaboration. It emphasizes that AI should augment human work rather than replace it.
  • AI as a Tool: AI supports human decision-making by providing data-driven insights.
  • AI as a Collaborator: AI assists humans by sharing tasks and improving productivity.
  • AI as a Manager: AI takes over specific management functions, such as scheduling or performance monitoring.
The “Human-in-the-Loop” AI Model (MIT) ensures that humans remain integral to AI processes, particularly for tasks requiring judgment, ethics, and creativity.

Source: Human in the Loop AI Training


  • Active learning is a form of human-in-the-loop training in which the model selects the data to be annotated. This helps in rapid model iteration and reduces by 10x annotation requirements, making these models augmentative ready and efficient. AI Automation: 
    • Tasks AI can handle entirely.
    • Human-in-the-Loop: Tasks where humans make critical decisions or review AI outputs.
    • Human Override: Tasks where humans can override AI outputs in sensitive areas.


PwC’s AI Augmentation Spectrum highlights six stages of human-AI collaboration:

Source: Human-AI Augumentation Model (pwc)

PWC believes that content generation will fall into a couple of scenarios: human-created, AI-created, AI-created and validated, and AI-created augmented. And this will evolve a process that builds an exciting and creative AI-augmented human society with a broad spectrum of co-creation. AI as an Advisor: Providing insights and recommendations.
    • AI as an Assistant: Helping humans perform tasks more efficiently.
    • AI as a Co-Creator: Working collaboratively on tasks.
    • AI as an Executor: Performing tasks with minimal human input.
    • AI as a Decision-Maker: Making decisions independently.
    • AI as a Self-Learner: Learning from tasks to improve over time.


Wednesday, January 29, 2025

Agentic AI Frameworks to continue to watch out for in 2025

The evolution of Agentic AI is unlocking new frontiers in autonomous systems and intelligent agents. As we enter 2025, developers and enterprises are looking for frameworks that streamline AI workflows and enable complex decision-making and collaboration. In this blog post I will explore the top five Agentic AI frameworks that are set to transform the AI landscape in 2025.

1. Microsoft AutoGen: Orchestrating Multi-Agent Systems

https://www.microsoft.com/en-us/research/project/autogen/

Microsoft AutoGen is redefining the way we build autonomous, event-driven systems. This framework specializes in orchestrating multiple AI agents to solve complex problems in a distributed environment.

🔹 Key Features:

  • Event-driven architecture for better scalability.
  • Support for integrating agents with APIs and external tools.
  • Advanced reasoning and task prioritization capabilities.

🔹 Why It Matters:
Microsoft AutoGen provides unparalleled support for creating systems that require multi-agent collaboration, making it ideal for use cases like IT infrastructure management and cloud automation.

🔹 Use Case:
Imagine an AI-driven cloud system that autonomously resolves server misconfigurations by delegating tasks to specialized agents or say you can envision a smart customer support system where AI agents collaborate to handle inquiries. One agent processes incoming queries, another retrieves relevant knowledge base articles, and a third escalates complex issues to human agents—ensuring fast, accurate, and seamless support.

🔹Notebook: https://github.com/microsoft/autogen?tab=readme-ov-file

2. LangChain: Building Seamless AI Workflows

https://blog.langchain.dev/how-to-design-an-agent-for-production/

LangChain has become a key player in workflow automation within the AI industry. It enables developers to link prompts, memory, and tools into cohesive pipelines, simplifying the process of building complex applications powered by large language models (LLMs). 

🔹 Key Features:- 

  • Modular components for creating and managing workflows.
  • Integrated memory for stateful applications. 
  • Compatibility with multiple LLMs and APIs. 

🔹 Why It Matters: LangChain’s flexibility has made it a favorite among developers for building conversational agents, retrieval-augmented systems, and more. 

🔹 Use Case:  Creating a chatbot for a financial institution that can efficiently retrieve and summarize client data. 

🔹 Notebook:** https://colab.research.google.com/github/langchain-ai/langchain/blob/master/docs/docs/tutorials/agents.ipynb#scrollTo=17546ebb

3. LangGraph: Advanced State Management

https://blog.langchain.dev/langgraph-multi-agent-workflows/

LangGraph leverages graph-based architectures to manage stateful AI workflows. This framework is tailored for applications requiring dependency management and logical flow across multi-step tasks.

🔹 Key Features:

  • Dependency graph-based workflow handling.
  • Simplifies complex stateful systems with advanced logic.
  • Flexible API integration for real-world applications.

🔹 Why It Matters: LangGraph is perfect for industries that require precise, sequential task execution, such as healthcare or supply chain management.

🔹 Use Case: Developing an AI agent for medical diagnosis that handles a multi-step process, from retrieving patient history to recommending treatments.

🔹Notebook: https://github.com/langchain-ai/langgraph

4. Microsoft Semantic Kernel: Bridging Semantics with Functionality

https://learn.microsoft.com/en-us/semantic-kernel/overview/

The Semantic Kernel by Microsoft focuses on contextual understanding and semantic reasoning. This framework is designed to merge the power of semantic AI with software development.

🔹 Key Features:

  • Context-aware tools for real-world applications.
  • Semantic reasoning to understand user intent better.
  • Pre-built connectors for seamless integration into business systems.

🔹 Why It Matters:
By embedding semantic understanding into applications, Semantic Kernel improves decision-making accuracy in industries like customer service and IT operations.

🔹 Use Case:
Enhancing an IT helpdesk AI agent to understand user intent and execute commands efficiently.

🔹Notebook: https://github.com/microsoft/semantic-kernel

5. CrewAI: Multi-Agent Collaboration Made Easy

https://docs.crewai.com/introduction

CrewAI is paving the way for collaborative agent systems, focusing on task execution by multiple agents. With its emphasis on coordination and communication, CrewAI is perfect for building intelligent agentic ecosystems.

🔹 Key Features:

  • Collaboration-focused design for agent teamwork.
  • Built-in task planning and delegation mechanisms.
  • Customizable modules for various industries.

🔹 Why It Matters:
CrewAI’s ability to handle multi-agent collaboration makes it a go-to framework for projects requiring team-based problem-solving, like logistics and resource planning.

🔹 Use Case:
Creating a fleet management system where agents collaboratively plan optimal delivery routes.

🔹Notebook:https://github.com/crewAIInc/crewAI

Why These Frameworks Matter

These frameworks are more than just tools — they’re blueprints for the future of Agentic AI. As businesses demand smarter, more autonomous systems, adopting the right framework will be critical for staying competitive in 2025

There are lot of Youtube videos from content creators that teach you how to create Agentic AI solutions take it further after you have browsed and learnt them and try making agentic AI systems that can be reused or chained together to solve complex workflows. 

Cheers, 

Thursday, January 23, 2025

Missional & Cultural Fit vis vis Technical and Team Fit. - Random thoughts on hiring and designing interviewing templates

 

In my leadership and consulting roles with various organizations over my tenures, I am sometimes amused, shocked, and not the least surprised by the lack of structured interviewing methodologies hiring managers or director-level leadership uses. Over the years, I have developed a simple template for non-profits that could be easily tweaked and replicated for a profit organization equally. The idea is to have interview panelists divide the questions on the interview questions among themselves and have each member of the interview team score all questions irrespective of who is asking the question. The idea is to have some structure (so panelists are not struggling to ask questions or the right questions) to get to know the prospective candidate to capture observations and score each question answered. 

The template assesses the candidate in four areas: Missional Fit, Team Fit, Cultural Fit, and Technical Fit. 



After the interview, the panelists collectively review their scores and discuss. This allows them to understand each other's scoring rationale and share qualitative observations, fostering a collaborative and unified decision-making process.  It is recommended that someone from one or two departments' key leaders or stakeholders also participate with the hiring manager and the panel interviewing the candidate who has applied. This provides an interesting vantage point for observations and scoring, allowing us to collaborate more and overcome biases as much as possible. 

In the past, it's incredible how different scores and qualitative observations from various team members give the hiring manager different perspectives and foster a sense of unity that is part of transformative cultures. This simple tool can easily be plotted on a four-quadrant or two-quadrant x-y model, with the x-axis measuring missional and character scores and the y-axis plotting Team fit and Technical fit components. We have scores plotting more to the upper right-hand corner, and the cluster of new hires who form the team increases. You are on your way to building great teams that have closer congruency within your team and your own organizational cultures. 


I have noticed there are times when a prospective candidate may not be a very high missional fit but scores well on the other three vectors, and this is where managerial and leadership decision-making becomes an art rather than a science where discernment, unity, and collaborative discussions enable you to come to sound decisions. It is an opportunity to gauge if a prospective candidate (internal incumbent) or an external candidate can provide opportunities and challenges in providing mentorship, coaching, and encouragement or allowing the team to pause before making an offer. The opposite could also be true if the individual exhibits a strong missional and cultural fit but lacks the core competency of technical acumen needed along with being a team fit (jelling well with the team they are going to be on)- discussion between the panelists of pros and cons will allow them to come to a place of unity and sound decision-making. 

After all, you, as a leader, are only strong if you have a great team and team members who support your organization's vision, mission, and strategies. 

Tuesday, January 14, 2025

Guessing what's going to be new in AI for 2025

 AI has been the buzz technology for the last two or three years in the main stream with LLM's like chatgpt and a host of variants that flooded our market but as I like to guess/foresee where this buzz and real progress is going to take its kinda of fun based on reading to collate some thoughts here.  I will categorize this into three broad strokes. Large language models that have some kind of reasoning capability, ability to create virtual playgrounds with context and AI applications in scientific research. 

Large Language models Becoming more intelligent (able to reason ...kind of) 

Here’s where things get even more interesting. When OpenAI dropped o1 in September, it didn’t just introduce a new language model; it sparked a paradigm shift. Two months later, o3 took things further, pushing boundaries we didn’t even know existed. Unlike traditional models like GPT-4, which spit out answers as they come to mind—sometimes right, sometimes wrong—these new models are designed to think through their responses. They break down complex problems into manageable steps, trying one approach after another until they get it right. This so-called “reasoning” capability (we know, the term is loaded) is game-changing for accuracy, especially in math, physics, and logic. And let’s be honest, it’s a crucial leap for AI agents. What I like about this in practical applications is when chatbots to whatever agentic AI models we come up with are able to build on context and add that context to some sort of learning model and then keep building that knowledge-base in the background. 

Take for example  Sal Khan in his TED talk demonstrated this with the next evolution of Khan academy

  • Scenario: A student struggling with calculus uses an AI tutor for help. Older models provide step-by-step solutions to specific problems but don't adapt well to different learning styles or gaps in foundational knowledge.
  • With Reasoning AI: The AI identifies the student’s weak points, breaking complex problems into smaller, manageable steps while adjusting explanations to match their understanding. If one explanation doesn’t resonate, the AI tries another approach, like visualizing the problem with graphs or interactive models. Sal suggests that Khan Academy will have the ability like a personal tutor to carry a conversation with a student and make suggestions that help in thinking about problems and reason through it with AI chat augumenting it. 
  • Outcome: More effective learning tailored to individual needs.

    AI Is Booming or a Boon to Boost Science

     AI is accelerating scientific discovery. Last October Nobel Prize winners in Chemistry to Demis Hassabis and John M. Jumper of Google DeepMind for their work on AlphaFold,  cracked the protein-folding problem, and to David Baker for tools that design entirely new proteins. This wasn’t just a win for AI; it was a massive step forward for humanity. So based on this trend in natual sciences so expect 2025 to bring a surge of data sets and models targeted at unlocking the mysteries of the natural world. Proteins were just the beginning because they had the perfect data sets for training AI. The hunt is on for the next big breakthrough—and it’s anyone’s guess what that will be.

    Virtual Playgorunds 

  • If 2023 was all about generative images and 2024 brought us generative video, what’s the next frontier? You guessed it—generative virtual worlds, aka video games. And this isn’t some far-off dream; the groundwork is already being laid. Back in February, Google DeepMind gave us a sneak peek of what’s possible with their generative model, Genie, which turned a single still image into an interactive 2D side-scrolling game. Then in December, they upped the ante with Genie 2—a model capable of transforming a starter image into an entire interactive virtual world. And it’s not just Google. Other companies are racing to develop similar technology. 2025 could be the year where the line between gaming and AI artistry blurs completely.

    Last February Genie 1 showed us the ability to transform a single still image into an interactive 2D side-scrolling game, similar to classic platformers like Mario Bros. This innovation allowed users to upload any image and watch it evolve into a playable environment, complete with logic and physics rules generated by AI. In December: Genie 2 expanded this capability by creating fully immersive virtual worlds from a starter image, enabling more complex environments, characters, and storylines. Imagine sketching a tree, and the AI generates an entire forest biome with interactive flora, fauna, and unique weather patterns. 

    Another good example will  be Sony AI and Procedural Storytelling
    • The Vision: Imagine a PlayStation game where players make decisions, and AI generates the story dynamically. Instead of pre-written narratives, every player experiences a unique storyline, shaped by their choices and the AI’s ability to adapt in real time.
    • Practical Example: A fantasy RPG where your interactions with NPCs generate unique quests, alliances, and outcomes that vary dramatically from other players’ experiences.

    Mojang Studios brings generative AI into Minecraft, and suddenly, building your dream world is as simple as describing it. I could just say, “Create a volcanic island with a hidden temple and treasure,” and—like magic—the game would bring it to life in real-time. Picture a fully realized environment, complete with fiery lava flows, intricate challenges, and treasures waiting to be uncovered. No painstaking block-by-block construction, just pure creativity unleashed.

    2025 isn’t just another year for AI; it’s shaping up to be a year of extraordinary leaps. So, buckle up—the future’s unfolding faster than we ever imagined.

    Thursday, January 9, 2025

    Data Insights - Setting SMART Goals

    As a habit for continuous improvement, I want to return to this space and keep posting. Last year was very busy, hence the massive gap in not recording and writing. 

    Mastering the ability to harness and interpret data is essential for success in any organization. In this post, I want to oversimplify SMART goals for my own sake to develop clear, data-driven goals that position you as the go-to person for transforming data into actionable strategies. The famous SMART Goal framework, which stands for Specific, Measurable, Achievable, Relevant, and Time-bound, will help to achieve this. Lately, I have been thinking about how an email campaign goal would be defined for one of our product's marketing goals. Let's use it as a running example to illustrate improving email marketing performance.


    1. Be Specific

    Specific goals provide a clear direction and eliminate ambiguity. Define your objectives with precision so everyone involved understands what's expected. For example, instead of a vague goal like "improve email performance," a specific goal would be:
    "Increase the email open rate by 15% in the next quarter by refining subject lines and targeting segmented audiences."


    This clarity ensures alignment across your team and integrates the goal into the larger business context.


    2. Make Goals Measurable

    Measurable outcomes are the heart of any data-driven strategy. Without clear metrics, decision-making becomes guesswork. In email marketing, measurable metrics include open rates, click-through rates, and conversions.
    "Track open rates weekly, aiming for an increase from 20% to 35%, and set a benchmark click-through rate of 5%."
    This ensures you can monitor progress and refine strategies as needed.


    3. Set Achievable Goals

    While ambition is valuable, goals must remain realistic. Unrealistic targets can lead to frustration and missed opportunities. For example, if your current open rate is 20%, aiming for 50% in just one month might not be feasible.
    "Set an achievable target of increasing open rates to 35% over the next three months by implementing A/B testing and optimizing email timing."
    This approach provides a reasonable target while encouraging steady improvement.


    4. Focus on Relevance

    Your goals must align with your organization's priorities. Ask yourself, "How does this goal support the business's growth or objectives?" In our example, improved email performance is directly relevant if the organization relies on email campaigns for lead generation or customer engagement.
    "Focus on improving open rates because higher engagement will drive more traffic to our website, supporting our goal of increasing sales conversions by 10% this quarter."
    This ensures the goal contributes meaningfully to broader business objectives.


    5. Make Goals Time-Bound

    Practical goals require deadlines. A time-bound goal ensures accountability and keeps efforts aligned with organizational priorities. For the email marketing example, set a specific timeline:
    "Achieve a 15% increase in open rates and a 5% click-through rate by the end of the next quarter (90 days)."
    This creates a clear deadline to measure progress and maintain momentum.


    By following the SMART framework and applying it to actionable examples like email marketing, you can create measurable, realistic, and impactful strategies that align with your organization's goals and drive success.

    Security Practices Questions