Artificial Intelligence & ChatGPT for Cyber Security 2026

Explore prompt engineering in generative AI for cyber security by using system/role prompts and custom instructions to shape structured outputs, with an Alex-style cyber security analyst persona.

Explore few-shot prompting to improve ChatGPT responses by using role prompts, structured outputs, and example-driven validation across code analysis scenarios like SQL injection and cross-site scripting, plus authentication best practices.

Discover how chain of thought prompting breaks complex cybersecurity tasks into small chunks and iteratively builds detailed analyses, including pros and cons and integration with existing systems.

Practice analyzing cybersecurity log files using prompt engineering with GPT-4 by uploading the Cybersecurity Logs.txt, crafting prompts with role, few-shot, or chain-of-thought approaches to extract insights.

Extracts data from log files using system and role prompting, organizing date, IP, username, and action into a table. Produces Python code to analyze and visualize future cybersecurity logs.

Build a custom instruction by defining a cybersecurity analyst role named Alex, setting context and guidelines, and applying few-shot prompting for credible, holistic responses.

Explore how generative AI and ChatGPT use Bing browsing to gather and summarize internet information for security research, including access management, session hijacking, and AI driven security mechanisms.

Explore advanced ChatGPT techniques, including browsing with Bing for up-to-date cybersecurity insights, data analysis with user datasets, image generation with Dall-E 3, and plugin integrations to access external data.

Upload large log files to ChatGPT and write a prompt to summarize and extract crucial points, guiding your research on the heavy file using GPT-4.

Compare how ai tools like Bart, ChatGPT, Bing Chat, Claudii, and Cloud AI handle prompts, extract main cyber security themes, and support research with logs and resources.

Protect personal and company data when using ChatGPT by avoiding private data upload, anonymizing when necessary, turning off chat history, and leveraging enterprise or API options for private environments.

Explore how social engineering and phishing exploit human psychology to breach cybersecurity, with email and voice calls using AI tools like voice cloning to trick employees into revealing passwords.

Learn how to clone a voice with ElevenLabs, using text-to-speech and instant voice cloning, including steps to upload samples while emphasizing consent and ethical use.

Explore how to clone voices with Resemble AI, compare it to 11 Labs, test with the Samantha voice, and use the editor features and speech-to-speech options under the free 280-character limit.

Explore creating deepfakes with d-id by generating AI avatar videos from a text script, choosing language and voice, and considering ethical use with free credits and subscriptions.

Demonstrates using ChatGPT to write emails in another person's style by feeding their LinkedIn posts, showing how a single post guides tone, and highlighting social engineering risks.

Identify phishing and social engineering across email, voice, and video by checking sender addresses, scrutinizing links, and avoiding requests for personal data, while using official channels and AI-based detection.

Explore how AI-powered SIEM systems enhance real-time monitoring, data analysis, automated incident response, and threat detection.

Explore how AI driven firewalls outperform traditional rule-based systems with dynamic learning, real-time protection, and context-aware analysis, bridging host-based, network-based, and next-gen firewall technologies.

Explore how AI-based email filtering detects spam and phishing using similarity-based, sample-based, and adaptive algorithms, flags indicators like malicious IP addresses, URLs, and suspicious content, and balances security with usability.

Explore how ai strengthens identity and access management with biometric authentication, behavioral analytics, risk-based access control, adaptive multi-factor authentication, and password management.

Explore how ai enhances ids and ips with real-time threat detection, anomaly-based alerts, and automated responses across hids and nids through autonomous systems for proactive security.

Explore how artificial intelligence strengthens email security and filtering, covering spam detection, phishing protection, malware scanning, content and behavior analysis, and data protection under GDPR.

Learn how spam filters protect inbox hygiene by analyzing headers and content. Use blacklist and whitelist checks, Bayesian filtering, and machine learning to detect spam, phishing, and malware.

Learn to use Google Colab, a browser-based cloud Python IDE that runs without local installation. Create notebooks, upload data, run code in cells, and enable GPUs.

Learn to create a copy of a workbook and save it to your drive to preserve progress, then edit the copied version without altering the original malware detection system workbook.

Develop an email spam detection system using machine learning to classify emails as spam or not spam, and explore deploying it to your own email provider.

Explore loading and inspecting a spam dataset using NumPy, pandas, Seaborn, and Matplotlib, download and upload csv data from Kaggle, and preview data with head for model-ready analysis.

Analyze an imbalanced email dataset to distinguish spam from ham, visualize class counts with Matplotlib and Seaborn, and implement a get most common words function.

Explore data analysis steps for text data, from counting common words and detecting data balance to feature engineering with text length, group by and describe for spam vs ham.

Learn text data preprocessing for machine learning by cleaning raw text, lowering case, removing punctuation and English stop words, and creating a clean text column for spam detection.

Transform email text into numeric features using bag of words and tf-idf, then vectorize with sklearn, and split data for training and testing to build a spam detector.

Explore the k nearest neighbors algorithm, its intuition, and how to use odd k and Euclidean distance to classify new data by majority vote of labeled examples (spam vs ham).

Train a k nearest neighbors classifier to detect spam emails using sklearn, and evaluate accuracy on the test data, exploring how changing the number of neighbors affects performance.

Learn how to build a spam detection system using OpenAI's GPT-4 API, compare it with in-house kNN methods, and manage costs, latency, and privacy.

Identify phishing types—email, spear phishing, vishing, smishing, farming, and whaling—and recognize telltale signs like spoofed addresses, urgency, and suspicious links.

Learn to recognize and mitigate phishing attacks by checking URLs, http vs https, spelling and branding cues, certificates, and using real-time, ML-based detection tools and awareness training.

Develop a phishing detection classifier using a 48-feature dataset, loading and exploring the data, and evaluating performance with train-test splits on a Kaggle phishing dataset.

Analyze a new data set by inspecting structure and missing values with data.info and describe, then drop uninformative features like id, and prepare data for train-test split.

Split the data into features and targets, create training and test sets with train_test_split at 25% test and random_state 42, preparing for the upcoming random forest algorithm.

Discover how decision trees classify data into multiple classes by splitting on feature thresholds, and why random forests combine many trees for robust predictions.

Learn how to train a random forest classifier with sklearn to classify phishing websites, using ensemble learning and default settings, achieving about 98% accuracy and exploring evaluation metrics.

Learn how precision and recall supplement accuracy for binary classification in cybersecurity. Use true positives, false positives, and false negatives with phishing detection and fraud examples.

Investigate how network security uses firewalls, ids/ips, and siem to detect threats, with manual monitoring and ai-driven tools enhancing defense.

learn to build a network anomaly detection system using machine learning on a network intrusion dataset, tackling binary and multi-class classification with data loading via pandas and preprocessing pipelines.

Preprocess the network anomaly dataset by inspecting descriptions, removing non-informative zero-heavy columns, handling missing values, and preparing a clean, numeric-ready dataset for modeling.

Prepare the network anomaly dataset by converting the attack target to normal-vs-attack and encoding protocol type, service, and flag; then normalize and split for logistic regression binary classification.

Explore logistic regression, a binary classification algorithm, to predict churn using data points and a probability threshold learned via maximum likelihood.

Implement logistic regression using sklearn, train x_train and y_train, predict x_test, achieving 98% accuracy in attack detection; handle missing values by filling zeros and explore grid search to optimize.

Automate hyperparameter optimization with grid search and cross validation to find the best logistic regression parameters, including C and L1/L2 penalties, improving accuracy and robustness.

Explore how malware harms and compromises systems, and compare viruses, worms, trojans, ransomware, rootkits, keyloggers, and spyware while examining detection strategies.

Explains traditional malware detection methods and essential defenses, including cautious downloading, patching, antivirus, firewall, least privilege, and multi-factor authentication, plus signature, heuristic, sandboxing, network, and machine learning techniques.

Load and analyze a 79-column malware dataset from Kaggle, evaluate multiple algorithms with metrics, and select the best model for detecting malware versus normal files.

Analyze data types and null values, drop noninformative columns, scale features with min-max, and create stratified train/test splits to prepare a malware dataset for machine learning.

Explore multiple machine learning algorithms—k nearest neighbors, random forest, and logistic regression—evaluate accuracy on train and test data, and save the best model for deployment.

Save the trained malware detection model with pickle and its scaler, so you can reload and run predictions in production with 99% accuracy.

Explore how data poisoning taints training data, biases ai models, and degrades performance, with defenses like robust training, data quality checks, adversarial training, and anomaly detection.

Identify data bias as distortions that cause inaccurate or unfair AI outcomes, distinct from data poisoning. Outline mitigation steps such as diverse data, bias detection, ethical collection, and algorithmic fairness.

Explore common AI model vulnerabilities, including prompt injections, data leakage, unauthorized code execution, and alignment issues, and learn prevention strategies like input sanitization, output filtering, sandboxing, and regular auditing.

Examine the yin and yang of artificial intelligence, balancing benefits with concerns like bias, transparency, privacy, and accountability. Learn how autonomy, job displacement, and security risks shape artificial intelligence deployment.