Optimizing Cybersecurity Costs with FinOps
This blog highlights the integration of two essential technologies: Cybersecurity best practices and effective budget management across tools and services. Let’s understand FinOps FinOps is a cultural practice for cloud cost management. It enables teams to take ownership of cloud usage. It helps organizations maximize value by fostering collaboration among technology, finance, and business teams on data-driven spending decisions. FinOps Framework The FinOps Framework works across the following areas: Principles Collaborate as a team. Take responsibility for cloud resources. Ensure timely access to reports. Phases Inform: Visibility and allocation Optimize: Utilization Operate: Continuous improvement and operations Maturity: Crawl, Walk, Run Key Components of Cybersecurity Budgets Preventive Measures Preventive measures serve as the initial line of defense in cybersecurity. These measures encompass firewalls, antivirus software, and encryption tools. The primary objective of these measures is to avert cybersecurity incidents from occurring. They constitute a critical component of any comprehensive cybersecurity strategy and often account for a substantial portion of the budget. Detection & Monitoring Tools like Azure Firewalls and Azure monitoring are essential for identifying potential security threats and alerting teams early to minimize impact. Incident Response Incident response comprises the measures taken to mitigate the impact of a security breach after its occurrence. This process includes isolating compromised systems, eliminating malicious software, and restoring affected systems to their normal functionality Training & Awareness Training and awareness are crucial for cybersecurity. Educating employees about threats, teach them how to avoid risks, and inform them of company security policies. Investing in training can prevent security incidents. FinOps approach to managing the cost of Security Security Cost-Optimization Security is crucial as threats and cyber-attacks evolve. Azure FinOps helps identify and remove cloud spending inefficiencies, allowing resources to be reallocated to advanced threat detection, robust controls like MFA and ZTNA, and continuous monitoring tools. Azure FinOps provides visibility into cloud costs, identifying underutilized or redundant resources and over-provisioned budgets that can be redirected to cybersecurity. Continuous real-time monitoring helps spot trends, anomalies, and inefficiencies, aligning resources with strategic goals. Regular audits may reveal overlapping subscriptions or unused security features, while ongoing monitoring prevents these issues from recurring. The efficiency gained can fund advanced threat detection, new protection measures, or security training. FinOps ensures every dollar spent on cloud services adds value, transforming waste into a secure, efficient cloud environment. Risk Mitigation FinOps boosts visibility and transparency, helping teams find weaknesses and risks in licenses, identities, devices, and access points. This is crucial for improving IAM, configuring access controls correctly, and using MFA to protect systems and data, also involves continuous monitoring to spot security gaps early and align measures with organizational goals. It helps manage financial risk by estimating breach costs and allocating resources efficiently. Regular risk assessments and budget adjustments ensure effective security investments that balance defense and business objectives. Improved Compliance and Governance Complying with standards like GDPR, HIPAA, or PCI-DSS is essential for strong cyber defenses. A FinOps approach helps by automating compliance reporting, allowing organizations to use cost-effective tools such as Azure FinOps toolkit to meet regulations. Conclusion Azure FinOps is a useful tool for managing cybersecurity costs. It enhances cost visibility and accountability, enables budget optimization and assists with compliance audits and reporting, also helps businesses invest their resources effectively and efficiently.Microsoft Purview Best Practices
Microsoft Purview is a solution that helps organizations manage data and compliance. It also uses AI to classify data, monitor compliance, and identify risks. Key features include data discovery, classification, governence, retention, compliance management, encryption, and access controls. Purview ensures data security, prevents insider threats, and helps implement data loss prevention policies to meet compliance requirements. Hello everyone - This is just a short introduction, I am Dogan Colak. I have been working as an M365 Consultant for about 5 years, holding certifications such as MCT, SC-100, SC-200, SC-300, and MS-102, with a focus on Security & Compliance. This year, I am excited to share what I have learned with the Microsoft Technology Community. In the coming days, I will be publishing videos and articles based on the training agenda I have created. I will also share these articles on LinkedIn, so feel free to follow me there. I am always open to feedback and suggestions. See you soon!Streamlining AI Compliance: Introducing the Premium Template for Indonesia's PDP Law in Purview
In today’s evolving regulatory environment, businesses must navigate complex data privacy laws while fostering customer trust, especially as AI transforms industries. To support organizations in meeting compliance requirements, we’re introducing the Premium Assessment Template for Indonesia's Personal Data Protection (PDP) Law within Microsoft Purview Compliance Manager. This powerful tool automates critical compliance tasks, simplifies assessments, and integrates seamlessly with Microsoft’s E5 security and Purview solutions, helping businesses reduce manual effort and ensure compliance more efficiently. Discover how this template can streamline your compliance efforts and build trust in an AI-driven world.
New Blog | Architecting secure Gen AI applications: Preventing Indirect Prompt Injection Attacks
By Roee Oz As developers, we must be vigilant about how attackers could misuse our applications. While maximizing the capabilities of Generative AI (Gen-AI) is desirable, it's essential to balance this with security measures to prevent abuse. In a recent blog post, we discussed how a Gen AI application should use user identities for accessing sensitive data and performing sensitive operations. This practice reduces the risk of jailbreak and prompt injections, preventing malicious users from gaining access to resources they don’t have permissions to. However, what if an attacker manages to run a prompt under the identity of a valid user? An attacker can hide a prompt in an incoming document or email, and if a non-suspecting user uses a Gen-AI large language model (LLM) application to summarize the document or reply to the email, the attacker’s prompt may be executed on behalf of the end user. This is called indirect prompt injection. Let's start with some definitions: Prompt injection vulnerability occurs when an attacker manipulates a large language model (LLM) through crafted inputs, causing the LLM to unknowingly execute the attacker's intentions. This can be done directly by "jailbreaking" the system prompt or indirectly through manipulated external inputs, potentially leading to data exfiltration, social engineering, and other issues. Direct prompt injections, also known as "jailbreaking," occur when a malicious user overwrites or reveals the underlying system prompt. This allows attackers to exploit backend systems by interacting with insecure functions and data stores accessible through the LLM. Indirect Prompt Injections occur when an LLM accepts input from external sources that can be controlled by an attacker, such as websites or files. The attacker may embed a prompt injection in the external content, hijacking the conversation context. This can lead to unstable LLM output, allowing the attacker to manipulate the LLM or additional systems that the LLM can access. Also, indirect prompt injections do not need to be human-visible/readable, if the text is parsed by the LLM. Read the full post here: Architecting secure Gen AI applications: Preventing Indirect Prompt Injection Attacks
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Beginner to Security Analyst
Hi Community, I have searched across the internet but cannot find a real-life example or path. I'm a complete beginner in the security field (Passed my SC900, AI900, AZ900). I want to become a security analyst (sc 200). That's just the exam but I know I need to dive into networking, KQL, powershell(I have experience in this), bash and eventually do some go & python programming. Is it wise for me to jump into sc200 by October then learn the rest as I go by? The aim is to switch careers in 5months. Or should I do sc400 then sc300 then sc200. I have some AD experience and a bit of helpdesk experience in triage. (If these questions have been asked before, please do point me to the right direction). Muchos gracias
