Rod’s Blog

Rod's Blog focuses on Microsoft Security and AI technologies, offering insights into cybersecurity best practices, the ethical use of AI, career advice in tech, and the integration of AI with security. It emphasizes the importance of certifications, mental resilience for professionals, and the evolving landscape of generative AI and cybersecurity.

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The hottest Substack posts of Rod’s Blog

And their main takeaways

Using Microsoft Sentinel to Monitor, Detect and Alert Bad AI Content

39 implied HN points • 12 Oct 23
🕹 Technology AI Cybersecurity Data Analysis
  1. Microsoft Sentinel can be used to monitor and detect bad AI content, but it is important to consider whether it is the most efficient use of resources.
  2. Organizations may choose to ingest AI data into Microsoft Sentinel, create a watchlist of bad content, and set up alerts to detect issues.
  3. Responsibilities for handling AI content alerts can be appropriately assigned to HR or relevant teams, rather than overwhelming security teams.

Tip: Catch up on Microsoft Security Copilot Over a Weekend

39 implied HN points • 11 Oct 23
🕹 Technology IT Documentation Newsletter
  1. You can generate a PDF for the entire Security Copilot documentation. The existing Docs produce a PDF of about 95 pages.
  2. The Security Copilot Docs are constantly being updated, so remember to produce new versions of the PDF from time-to-time.
  3. You can send documents to your Kindle library on Kindle devices and app using the 'Send to Kindle' feature, with end-to-end encryption for protection.

Must Learn AI Security Compendium 9: The Argument for Safe AI

39 implied HN points • 11 Oct 23
🕹 Technology AI Security Ethical AI Data Protection Transparency
  1. AI Security and Responsible AI are related and play a critical role in ensuring the ethical and safe use of artificial intelligence.
  2. By intertwining AI Security and Responsible AI, organizations can build AI systems that are trustworthy, reliable, and beneficial for society.
  3. Challenges and opportunities in AI security and responsible AI include protecting data, addressing bias and fairness, ensuring transparency, and upholding accountability.

Brief: Going Passwordless - the New Trend in Online Security

19 implied HN points • 08 Feb 24
🕹 Technology Security Authentication Microsoft Online Services Biometrics
  1. Passwordless authentication aims to improve security by eliminating the need for traditional passwords and using methods like biometrics or hardware tokens instead.
  2. Going passwordless reduces the risk of password breaches and phishing attacks, making the login process faster and more convenient for users.
  3. Challenges of going passwordless include user trust in new technologies, compatibility issues, privacy concerns, and suitability for certain online services.

The Ways Microsoft Copilot for Security Can Enhance Security Operations with Microsoft Purview

19 implied HN points • 08 Feb 24
🕹 Technology Security AI Data Governance Compliance Incident Management
  1. Microsoft Security Copilot enhances security by seamlessly integrating with Microsoft Purview, simplifying security policies and governance.
  2. The AI capabilities of Microsoft Security Copilot aid in proactive threat detection and response by analyzing data to identify potential risks before they escalate.
  3. Automated compliance and data governance processes are streamlined through the combination of Microsoft Purview's features and Security Copilot's automation, facilitating adherence to regulations.
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Azure OpenAI Content Filtering and Abuse Monitoring with Microsoft Sentinel

39 implied HN points • 10 Aug 23
🕹 Technology AI Security Azure Monitoring
  1. Microsoft Sentinel is a powerful tool for capturing and analyzing logs, primarily used for security purposes.
  2. Content filtering in Azure OpenAI detects and takes action on harmful content in both input prompts and output completions.
  3. Abuse monitoring in Azure OpenAI helps detect and mitigate instances of recurring content or behaviors that may violate the Code of Conduct or product terms.

Azure AI Studio Tip: Deploying to an Existing Web App Generates a Newly Registered AAD App Every Time

39 implied HN points • 04 Aug 23
🕹 Technology AI Azure Deployment
  1. Deploying updates to an existing Web App in Azure AI Studio creates a new AAD App registration every time, making it harder to manage the app.
  2. Having additional App registrations may not have a clear cost or performance impact, but it adds complexity in tracking and managing the most recent one.
  3. Assigning a real domain to your Web App means you'll need to find and update redirect URLs in Authentication for each newly registered AAD App to ensure user login functionality.

Must Learn AI Security Compendium 1: AI Cyber Attacks - The Rising Threat to Security

39 implied HN points • 07 Sep 23
🕹 Technology Deepfakes Quantum Computing Data Poisoning
  1. AI cyber attacks are on the rise, becoming more prevalent and sophisticated, targeting individuals and organizations using AI algorithms to evade traditional security measures.
  2. Hackers utilize AI-powered botnets in attacks like the TaskRabbit incident, which compromised millions of user accounts, exposing sensitive data such as Social Security numbers and bank account details.
  3. Deepfakes, evasion, oracle attacks, compromised AI systems, and quantum computing present serious threats, necessitating robust cybersecurity measures and proactive defense strategies to protect against evolving AI-enabled attacks.

Must Learn AI Security Part 18: Bias Exploitation Attacks Against AI

39 implied HN points • 29 Sep 23
🕹 Technology AI Security Adversarial Attacks
  1. A Bias Exploitation attack against AI manipulates an AI system's output by exploiting biases in its algorithms, leading to skewed and inaccurate results with potentially harmful consequences.
  2. Types of Bias Exploitation attacks include data poisoning, adversarial attacks, model inversion, backdoor attacks, and membership inference attacks - all aim to exploit biases in AI systems.
  3. Mitigating Bias Exploitation attacks involves using diverse and representative data, regularly auditing and updating AI systems, including ethical considerations in the design process, and educating users and stakeholders.

Must Learn AI Security Part 21: Watermark Removal Attacks Against AI

39 implied HN points • 05 Oct 23
🕹 Technology AI Security Machine Learning Adversarial Attacks Deepfakes
  1. A watermark removal attack against AI involves removing unique identifiers from digital images or videos, leading to unauthorized use and distribution of copyrighted content. This is illegal and can have legal consequences.
  2. Types of watermark removal attacks include image processing, machine learning, adversarial attacks, copy-move attacks, and blurring/masking attacks. These methods violate intellectual property rights.
  3. Mitigation strategies for watermark removal attacks include using robust and invisible watermarks, applying multiple watermarks, using detection tools, enforcing copyright laws, and educating users about the risks.

Microsoft Sentinel SOC 101: How to Detect and Mitigate Cryptojacking Attacks with Microsoft Sentinel

39 implied HN points • 03 Oct 23
🕹 Technology Cybersecurity Cloud Computing Data Analytics Threat Detection
  1. Cryptojacking involves using cloud resources to mine cryptocurrencies, leading to increased costs and performance issues for affected cloud customers.
  2. Common indicators of cryptojacking include high CPU/memory usage by unknown processes, unusual network traffic patterns, changes in cloud resource usage, and presence of malicious mining code.
  3. Microsoft Sentinel can help detect and respond to cryptojacking by analyzing data from various sources, applying advanced analytics, providing visualization dashboards, and enabling fast investigation and response using built-in playbooks.

Must Learn AI Security Part 16: Impersonation Attacks Against AI

39 implied HN points • 25 Sep 23
🕹 Technology Security AI Cyber Attacks Data Machine Learning
  1. Impersonation attacks against AI involve deceiving the system by pretending to be legitimate users to gain unauthorized access, control, or privileges. Robust security measures like encryption, authentication, and intrusion detection are crucial to protect AI systems from such attacks.
  2. Types of impersonation attacks include spoofing, adversarial attacks, Sybil attacks, replay attacks, man-in-the-middle attacks, and social engineering attacks. Each type targets different aspects of the system.
  3. To mitigate impersonation attacks against AI, organizations should implement strong security measures like authentication, encryption, access control, regular updates, and user education. Monitoring user behavior, system logs, network traffic, input and output data, and access control are essential for detecting and responding to such attacks.

Must Learn AI Security Part 14: Inference Attacks Against AI

39 implied HN points • 18 Sep 23
🕹 Technology AI Security Privacy Data Protection Security Measures
  1. An inference attack against AI involves gaining private information from a system by analyzing its outputs and other available data.
  2. There are two main types of inference attacks: model inversion attacks aim to reconstruct input data, while membership inference attacks try to determine if specific data points were part of the training dataset.
  3. To mitigate inference attacks, techniques like differential privacy, federated learning, secure multi-party computation, data obfuscation, access control, and regular model updates can be used.

Microsoft Sentinel SOC 101: How to Detect and Mitigate Credential Reuse Attacks with Microsoft Sentinel

39 implied HN points • 26 Sep 23
🕹 Technology Cybersecurity Data Analytics Threat Detection Risk Assessment
  1. Increase the cost of compromising an identity by banning common passwords, enforcing multi-factor authentication, and blocking legacy authentication.
  2. Detect threats through user behavior anomalies by ensuring event logging and data retention and by leveraging User and Entity Behavioral Analytics.
  3. Assess identity risk by conducting penetration tests, password spray tests, and simulated phishing campaigns to strengthen security controls.

Building Your own Conversational Copilot with Python, Flask, and Azure Open AI SDK

39 implied HN points • 25 Apr 23
🕹 Technology Programming Web Development AI Security
  1. The post discusses building a conversational copilot using Python, Flask, and Azure Open AI SDK.
  2. It highlights the importance of monitoring AI security, particularly focusing on Azure Open AI and Azure Cognitive services.
  3. The post provides details about the necessary code files and steps to run a web-based Chatbot using Python, Flask, and Azure Open AI SDK.

Must Learn AI Security Compendium 4: Leveraging Generative AI for Cybersecurity Defense

39 implied HN points • 19 Sep 23
🕹 Technology AI Security Cybersecurity Threat Detection
  1. Generative AI can enhance threat detection by analyzing patterns and behaviors to identify deviations and potential cyber threats.
  2. Using generative AI in cybersecurity can automate vulnerability analysis, streamlining the patching process and addressing weaknesses promptly.
  3. Generative AI can be leveraged to create decoy systems like honeypots to divert attackers, providing valuable insights to improve defense strategies.

Must Learn AI Security Part 15: Misinformation Attacks Against AI

39 implied HN points • 21 Sep 23
🕹 Technology AI Security Data Manipulation Threat Detection Cybersecurity
  1. Misinformation attacks against AI involve providing incorrect information to trick AI systems and manipulate their behavior.
  2. Types of misinformation attacks include adversarial examples, data poisoning, model inversion, Trojan attacks, membership inference attacks, and model stealing.
  3. Mitigating misinformation attacks requires data validation, robust model architectures, defense mechanisms, privacy-preserving techniques, monitoring, security best practices, user education, and collaborative efforts.

Must Learn AI Security Part 11: Denial-of-Service Attacks Against AI

39 implied HN points • 11 Sep 23
🕹 Technology Security AI Mitigation Monitoring
  1. Denial-of-Service (DoS) attacks against AI aim to overwhelm the system with requests, computations, or data, making it slow, crash, or become unresponsive.
  2. Common techniques used in DoS attacks against AI include request flooding, adversarial examples, amplification attacks, and exploiting vulnerabilities in the system.
  3. Effects of a DoS attack on an AI system can lead to unavailability, loss of productivity, financial loss, reputation damage, and increased security costs for the affected organization.

Must Learn AI Security Part 20: Text-based Attacks Against AI

39 implied HN points • 03 Oct 23
🕹 Technology AI Security Natural Language Processing Adversarial Attacks Cybersecurity
  1. Text-based attacks against AI target natural language processing systems like chatbots and virtual assistants by manipulating text to exploit vulnerabilities.
  2. Various types of text-based attacks include misclassification, adversarial examples, evasion attacks, poisoning attacks, and hidden text attacks which deceive AI systems with carefully crafted text.
  3. Text-based attacks against AI can lead to misinformation, security breaches, bias and discrimination, legal violations, and loss of trust, highlighting why organizations need to implement measures to detect and prevent such attacks.

Must Learn AI Security Part 7: Membership Inference Attacks Against AI

39 implied HN points • 24 Aug 23
🕹 Technology AI Security Privacy Data Protection Machine Learning
  1. Membership Inference Attacks against AI involve attackers trying to determine if a specific data point was part of a machine learning model's training dataset by analyzing the model's outputs.
  2. These attacks occur in steps like data collection, model access, creating shadow models, analyzing model outputs, and making inferences based on the analysis.
  3. The consequences of successful Membership Inference Attacks include privacy violations, data leakage, regulatory risks, trust erosion, and hindrance to data sharing in AI projects.

Using Azure Open AI with Microsoft Sentinel Part 1 - Getting Keys and Endpoints

39 implied HN points • 30 Mar 23
🕹 Technology Cloud Computing AI Data Management Cybersecurity Programming
  1. Consider transitioning from Logic App connector for Open AI ChatGPT to Azure Open AI's ChatGPT for more control over data.
  2. When working with Azure Open AI models, deployments should be done in the Azure console, not Azure OpenAI Studio, and need patience for the API to become accessible.
  3. In Microsoft Sentinel, use best practices like storing API keys and endpoints in Parameters for calls to Azure Open AI deployments.

Must Learn AI Security Part 5: Evasion Attacks Against AI

39 implied HN points • 22 Aug 23
🕹 Technology AI Security Cybersecurity
  1. Evasion attacks against AI involve deceiving AI systems to manipulate or exploit them, posing a serious security concern in areas like cybersecurity and fraud detection.
  2. Evasion attacks typically involve steps like identifying vulnerabilities, generating adversarial examples, submitting them to the AI system, and refining the attack if needed.
  3. These attacks can lead to compromised security, inaccurate decisions, bias, reduced trust in AI, increased costs, and reduced efficiency, highlighting the importance of developing defenses and detection mechanisms against them.

Using Azure Open AI with Microsoft Sentinel Part 2 - Converting Data to JSONL

39 implied HN points • 03 Apr 23
🕹 Technology AI OpenAI Microsoft Tools
  1. Azure Open AI supports JSONL file type for customized modeling, which is like JSON but with data values on a single line
  2. Tools like a PowerShell script and the Open AI CLI tool can help in converting JSON to JSONL for different data formats like CSV, TSV, XLSX, and JSON
  3. Deploy Azure Open AI instances in US South Central for access to base model types crucial for customized models

Must Learn KQL Part 4: Search for Fun and Profit

39 implied HN points • 31 May 23
🕹 Technology Data Analysis Cybersecurity
  1. The Kusto Query Language (KQL) search operator is a powerful tool for verifying the existence of certain elements within an environment.
  2. Using KQL for security purposes involves answering questions like 'Does it exist?', 'Where does it exist?', and 'Why does it exist?'
  3. KQL allows for detailed searches across specific tables in tools like Microsoft Office and Defender for Endpoint by leveraging wildcard characters.

Must Learn AI Security Part 3: Adversarial Attacks Against AI

39 implied HN points • 15 Aug 23
🕹 Technology AI Security Adversarial Attacks Monitoring
  1. Adversarial attacks against AI involve crafting sneaky input data to confuse AI systems and make them produce incorrect results.
  2. Different types of adversarial attacks include methods like FGSM, PGD, and DeepFool, each aiming to manipulate AI models in different ways.
  3. Mitigating adversarial attacks involves strategies like data augmentation, adversarial training, gradient masking, and ongoing research collaborations.

Microsoft Sentinel Content Hub Changes

39 implied HN points • 15 Jun 23
🕹 Technology Software Updates User Experience
  1. The default view in the Microsoft Sentinel Content Hub has changed to List Mode, which allows users to select multiple solutions for installation at once.
  2. The step-through wizard for installing solutions in the Content Hub has been replaced with simple options: Install and View Results.
  3. Investing in Microsoft Sentinel means having the most current version of the product available without downtime, showing continuous improvement in the platform.

Cross-workspace Query Best Practice for Microsoft Sentinel

39 implied HN points • 17 Apr 23
🕹 Technology Cybersecurity Cloud Computing Data Analysis Social media
  1. Cross-workspace queries in Microsoft Sentinel are crucial for managing multiple workspaces or customers.
  2. When using cross-workspace queries, it is more efficient to use the workspace ID rather than names or fully qualified names.
  3. Workspace IDs can be found in the Overview pane of the Log Analytics workspace or using a KQL query in Azure Resource Graph Explorer.

Defender for Cloud Learn Doc Gets Its Own RSS Feed

39 implied HN points • 08 Jun 23
🕹 Technology Software Updates Microsoft Information Security
  1. The Defender for Cloud Learn Doc now has its own RSS feed, granting users the ability to get notified about updates easily.
  2. Despite this improvement, not all pages on learn.microsoft.com have RSS feeds yet, so users still have to monitor some sections manually.
  3. Other Microsoft pages also have their own RSS feeds, showing an effort to provide users with up-to-date information through various channels.

Must Learn AI Security Part 6: Model Inversion Attacks Against AI

39 implied HN points • 23 Aug 23
🕹 Technology AI Security Privacy Data Breach Cybersecurity Machine Learning
  1. A Model Inversion attack against AI involves reconstructing training data by only having access to the model's output, posing risks to data privacy.
  2. There are two main types of Model Inversion attacks: black-box attack and white-box attack, differing in the level of access the attacker has to the AI model.
  3. Model Inversion attacks can have severe consequences like privacy violation, identity theft, loss of trust, legal issues, and misuse of sensitive information, emphasizing the need for robust security measures.

Must Learn AI Security Part 2: Data Poisoning Attacks Against AI

39 implied HN points • 08 Aug 23
🕹 Technology AI Security Data Poisoning Machine Learning Cybersecurity Monitoring
  1. Data Poisoning attacks aim to manipulate machine learning models by introducing misleading data during the training phase. Protecting data integrity is crucial in defending against these attacks.
  2. Data Poisoning attacks involve steps like targeting a model, injecting misleading data into the training set, training the model on this poisoned data, and exploiting the compromised model.
  3. These attacks can lead to loss of model integrity, confidentiality breaches, and damage to reputation. Monitoring data access, application activity, data validation, and model behavior are key strategies to mitigate Data Poisoning attacks.

Must Learn AI Security Compendium 7: What is Generative Automation?

39 implied HN points • 04 Oct 23
🕹 Technology AI Automation Security Challenges Generative
  1. Generative automation uses generative AI to automate tasks that require creativity or human-like reasoning, like writing a poem or designing a logo.
  2. Generative automation benefits various industries by helping with content creation, design, education, research, and more.
  3. Security challenges in generative automation include data security, access control, malicious code, third-party dependencies, human error, and lack of transparency.

Monitoring Updates to the Microsoft Sentinel GitHub Repo

39 implied HN points • 10 Mar 23
🕹 Technology GitHub
  1. The Microsoft Sentinel GitHub repository is essential for updates and new content, like Analytics Rules, Playbooks, and more.
  2. One way to stay updated is by using a Logic App that pulls RSS feed items and posts them to a Microsoft Teams channel.
  3. You can customize the RSS feed to filter out less relevant updates and choose where you want to receive notifications, such as Microsoft Teams, Inbox, or Slack.

Preparing the SOC for Generative AI

39 implied HN points • 05 Sep 23
🕹 Technology AI Cybersecurity Automation Podcasts Cloud Computing
  1. Before implementing Generative AI in a SOC, it's important to configure incident tags to provide more information for AI.
  2. Assigning specific incidents to analysts based on skillsets through automation rules can enhance SOC efficiency.
  3. Practicing gathering information to create better Generative AI prompts is crucial for successful AI utilization in a SOC.

Brief: How Microsoft AI Protects Your Data Privacy

19 implied HN points • 07 Feb 24
🕹 Technology AI Data Privacy Microsoft
  1. Microsoft AI is based on the principle of 'your data is your data', emphasizing that you own and control your personal data.
  2. Microsoft AI ensures data privacy by collecting and using data with consent, not selling data to third parties, and implementing strong security measures.
  3. Data privacy is crucial for AI as it builds trust, protects human rights and promotes innovation in the industry.

The KQL Mysteries: Chapter 8

19 implied HN points • 06 Feb 24
🕹 Technology Security Data Analysis Coding Cybersecurity IT
  1. A major security breach has occurred with sensitive data stolen, leading to a need for urgent action to track down the threat actor.
  2. Jordan quickly jumps into action, using KQL queries to analyze data and identify patterns associated with the suspected threat actor.
  3. The story leaves readers with a cliffhanger, hinting at upcoming developments and ensuring engagement for the next chapter.

Securing Data in an AI-First World with Microsoft Purview

19 implied HN points • 06 Feb 24
🕹 Technology Data security AI Microsoft Data Management Compliance
  1. Microsoft Purview is a top industry solution for managing data estates, offering governance, protection, and management.
  2. The latest enhancements to Microsoft Purview and Microsoft Defender focus on securing data in the context of generative AI, providing visibility, protection, and compliance controls.
  3. Organizations can leverage Microsoft Purview and Microsoft Defender to securely adopt AI, ensuring data protection while harnessing AI's full potential.

The Impact of AI on the Environment: A Critical Analysis

19 implied HN points • 05 Feb 24
🔬 Science Environment AI Data Research Education
  1. AI has both direct and indirect impacts on the environment. It can lead to high energy consumption and carbon emissions due to the computational complexity and rapid innovation cycle of AI systems.
  2. The way AI is used can either help or harm the environment. It can optimize energy efficiency and support sustainable development, but it can also increase resource demand, pollution, and disrupt ecosystems.
  3. To lessen the negative environmental effects of AI, collaborative efforts are essential. This includes implementing ethical guidelines, promoting green AI research, educating about AI's environmental impact, and incentivizing energy-efficient AI solutions.