And welcome to my talk titled Security best practices with against common vulnerabilities. My name is Kanadin. I'm a full stack web developer security researcher connected me on LinkedIn. I currently work as a backend developer at Star Technologies and a full stack of developer as a freelancer. Let's delve into the top, so we'll be covering introduction to Python security, commerce security threats in Python, secure coding practices in Python authentication and authorization, hashing and encryption in Python encryption and hashing, Python security tools and libraries, emerging threats and future consideration and finally a conclusion. So let's get right into it. Section one introduction of Python Security an overview of Python's popularity and relevance in software development. Python was officially released on the 20 February 1991 by Python Software foundation as the direction of video van Rosam. It has grown considerable heights over the years up to now. In our current time in our current time, Python ranks at number one in the PYPL index, that is the popularity of programming languages index. So we can see in the charts how Python was from 2005 up to number one in 2024 being utilized in the development community among both engineering and computing teams also in the advantage sector at large. This provides efficient efficiency of Python as a reliable development programming language. Python is also known to be used in large scale enterprises, some of which are listed below. So Python is used in Netflix, Google, Reddit, Spotify, Instagram, Dropbox. These are just a few of the numerous companies that make use of Python. The importance of security in Python based application security in Python web applications, applications in general has become a top priority and a crucial aspect to consider during development. Many Unix systems come with Python pre installed, making them potentially vulnerable to Python scripted attacks. If a Python application running a compromised unit space system lacks proper system measures, it's highly suspectible to attacks that could lead to the theft or leakage of sensitive information. On June 5, 2012, popular social network website LinkedIn was hacked. In this popular hack, the passwords were of over 6.5 million users. User accounts were stolen. Internet security experts said that the passwords were easy to unscramble because of LinkedIn's failure to use assault when hashing them. We'll cover that in ticker coding practices when we go over to section three, the password we are caught in as a result of an SQL injection attack. These attackers, the attackers exploited common vulnerabilities in a third party library used in LinkedIn to inject a malicious SQL code, granted them to fast amount of user data. LinkedIn apologized immediately and which seems like a very efficient response to the situation, probably after fixing the bug, most likely. See brief on the article's purpose and structure. This presentation aims to provide a clear and detailed insight to the essential security best practices in Python, web applications and general Python development. We will delve into the following areas. The common vulnerabilities and trade specific Python applications. We cover topics like SQL injection, cross site scripting, cross site crossford G secure coding practices in Python we'll handle image validation and standardization, proper use of libraries and dependencies secure file handling avoiding common pitfalls, that is, learn how to avoid common security mistakes that can lead to SQL eyes and xss. From there, we'll proceed to authentication and authorization method, the best practices implementing secure authorization schemes. We will then give a noteworthy coverage on encryption and hashing in Python, so let's delve into these topics. Section two common security threats in Python an overview of common vulnerabilities and threats specific to Python applications recognize that modern applications are inherently susceptible to security flaws. Some of these flaws are inherent to the technology stack, while others are common pitfalls in programming projects. Regardless of the source, virtually all application built with any programming language will inevitably have security vulnerabilities. Therefore, it falls on developers like ourselves to identify and address these vulnerabilities before malicious actors exploit them. Let's start with the first one. SQL injections SQL injection SQL ICE is a common and dangerous web security vulnerability that arises when malicious SQL code is injected into an application's impute and subsequently sent to the database. This allows attackers to manipulate the Internet SQL queries, potentially granting them unauthorized access to information or even control over the database itself. SQL injection can have devastating consequences, potentially leading to data theft. Hackers can state sensitive information like usernames, passwords, credit card information, and more. Data manipulation. They can modify or delete data, impacting website functionalities or causing financial losses. System takeover in extreme cases, they might even gain full control of database server. The next one is cross site scripting. XSS cross site scripting is a web security vulnerability that allows attackers to inject malicious scripts into websites. These scripts are then executed by unsuspecting users and browsers, potentially leading to data theft. Attackers can still seek information like cookies, session ids, and other data stored in the browser. Account takeover they can hijack user accounts by capturing login credentials or manipulating session data. The next one website defacement. They can alter website content or redirect users to malicious websites, which is really, really dangerous. Spreading malware they can inject malicious code into other websites visited by the user. Cross site request forgive CSRF cross site request forgery, also known as CSOf, is an attack that forces an end user to execute unwanted actions on a web application in which they are currently authenticated. They can have significant consequences like unauthorized transaction, data theft, website facement. The last one we'll cover is command injection. Command injection is a less popular attack popular injection attack compared to SQL injection attacks. This is because orchestrating such an attack takes time and consideration. However, overlooking command injection attacks can leave your system or application vulnerable to some big threats and in some cases lead to full system compromise. Command injection sends malicious data into an application that can lead to a grave damage that can lead to great damage when evaluated by the code interpreter. Simply put, this is when an attacker is able to execute common on your application server via a loophole in your application code. We could also call this remote code execution data theft. This could lead to data theft. Attackers can access sensitive information stored on the server's file system, database and other systems. System takeover they can gain control of the server, allowing them to install malware, delete files, or disrupt operations. Lateral movement they can use this compromised server as launching points to attack other systems on the network. Some other notable mentions includes attackers can also leverage on misconfiguration in our Python application, such as insecure file handling, insecure authentication, and insecure object references. Next section says next subsection says examples of fast security incidents in Python based application in Dropbox Dropbox in 2012, a large scale phishing attack compromised over 68 million user accounts, potentially leading exposing potentially exposing email addresses, names, and passwords hashed, but not something Reddit. In 2018, editor breach affected over 50 million user accounts, including usernames, user email addresses, and passwords hashed with SHS ShA, one considered weak at that time. That was 2018 Yahoo. In 2014, a series of data breaches compromised over 1 billion user accounts, exposing various personal information, including names, email addresses, phone numbers, and even dates. Then the next section says understanding the risks why Python applications are targeted Python popularity and ease of use makes it a target for attackers due to several inherent environmental factors. This report explores the key risks associated with Python web applications. Key vulnerabilities impute validation assess and sanitization inadequate impute handling can lead to SQL injection, xss, and command injection. Third party libraries vulnerable libraries introduce non cybersecurity risk into application framework specific vulnerabilities. Each framework has unique security consideration that require authentication that require attention. Preinstalled Python on Unix preinstallation can be convenient but requires consistent updates to adjust vulnerabilities. Then we have system permissions. Improper permissions can grant unauthorized access to sensitive files and directories. We are going to the next section, which is section three. Secure practices secure coding practices in Python, we start with impute validation and sanitization. Default line defense against injections that's what I like to call it. Impute validation is the process of ensuring data entered by user is valid and safe before it's been processed by the application. This is a crucial step in ensuring a Python application against securing a Python application against common vulnerabilities such as SQL injections and cross site scripting. Part of data validation which should be considered involves the following step trimming of data imputes regular expression and, for impute validation, enforcing the use of secure passwords numeric reach validation drop menu selection for impute validation, let's start with trimming of data impute. This simply involves removing leading or trailing spaces from user impute fields like names or addresses. Shimming ensures consistency and avoids unintentional white space in data storage or processing. This makes sure to utilize string functions like trim in Python or dedicated trimming libraries. Regular expression regex as usually call it and form validation validating imputex and email addresses to ensure they follow the standard custom formats. Using regex patterns regex helps enforce specific constraints on data formats, preventing invalid entries like missing ad symbols or incorrect domain names. Libraries such as re in Python offer rejects functionalities. Also, validation packages like django forms also support regex based validation, the percentages of secure passwords, checking password lengths, character types, that is, the uppercase, lowercase numbers, symbols, and disallowing common dictionary words. Strong password policies prevent weak and easily guessable passwords. Mitigated unauthorized access weeks libraries like passlib in Python can generate and hash passwords securely. Django uses decrypt, an industry standard algorithm known for its high computational costs and resistance to brute force attack. Drop menu selection. This is also a good option to consider as it limits the selectable options and application predefined list. This way, invalid or unsafe responses cannot be imputed as values. File upload validation this is a very important step as invalid file uploads have been known to create backdoor access or lead to reverse shell instances. Measures to validate files on the server side and never rely on client side validation alone. Always maintain updated versions of file validation libraries to ensure potential vulnerabilities to address potential vulnerabilities, here are some detailed considerations to take number one file type validation implement measures to check the file MIE MIME type to measures it matches allowed types that is image jpeg application Pdf format. Consider utilizing the Django's file content type property. Check the file extension against whitelist of permitted extensions such as JPeg PDf using the file Txt property in file size number two file size validation set the maximum file size allowed for upload using max upload size setting. Implement logic to restrict the total combined size of uploaded files per user or session here. These are efficient and effective to ensuring proper and safe file upload. These are very good practices. Malicious content detection leverage on antivirus libraries and all APIs to scan uploaded files. Malicious content make use of PU to validate the image integrity and upload potential vulnerabilities and prevent protecting vulnerabilities like cross site scripting attacks. Temporary storage consider using temporary storage locations for uploaded files before final processing and validation. The next one we have proper use of libraries and dependencies it is important to ensure proper use of libraries and dependencies in our Python application to help manage code lifecycle dependencies and outdated library vulnerabilities. When it comes to proper management of libraries and dependencies python development, it is necessary to state that it's necessary that the use of poetry is a first class solution worth mentioning and looking into. Poetry is a tool for dependency management and packaging in Python. Poetry basically allows you to declare libraries your project depends on and it will manage them for you. It is worth mentioning that poetry can also build your project for distribution. The next subsection goes handling sensitive data most of the time, sensitive data includes data designed by a business to be labeled as sensitive. This could be personal information, biodata, or information generated by a user while on an application. There are also information which should be generally considered as sensitive data, such as financial information. We can say that sensitive data is any data that an attacker values the most. That is definitely sensitive data, so you want to keep that data away from the attacker. To create methods to securely handle sensitive sensitive data, we think about what matters the most to application and to our users question on decentralization of this data and its security. We can as well leverage on platforms that offer authorization as a service, platforms like payments, IO, and piano. Steps to follow when implementing methods to handle sensitive data we can follow encryption, authentication, sanitization, login, and testing. All these steps help when it comes to handling sensitive data. Then you go to the last section, the D section, which says secure file handling. When it comes to handling files in python application, they are fully important actions carried out. Let's talk about some of them below. Number one, impute validation and sanitization. Start by validating file names. Restrict allowed characters and remove potentially harmful elements to prevent paths, traversal attacks, or code injection. Since sanitize file content depending on the context, consider techniques like antivirus scanning, image integrity checks, or content parsing for malicious code before processing. Validate file types and extension. Use backlist to accept only permitted file types such as JPEG or ptfs, and prevent unauthorized uploads that might introduce vulnerabilities. Number two secure storage store files in secure locations. Employ appropriate permissions and access control mechanisms. That is, change mode change ownership to restrict unauthorized access. Consider temporary storage. Use temporary directories or processing uploaded files before moving them to permanent storage locations, minimizing potential damage in case of vulnerabilities. Encrypt sensitive sensitive data in files. If files contain sensitive information, encrypt them at rest using libraries like ferrets and then decrypt them only when necessary. Good level best practices used context managers the first one says use context managers utilize context managers such as like open for opening files to ensure proper file closing and resource management, preventing potential vulnerabilities like leaving files open for unauthorized access. Avoid hard coded parts very important dynamically construct file paths based on user impute or other variables. Certain static parts might be exploitable. Utilize secure libraries employ well maintained and secure libraries for specific file handling tags e. G. OS path for basic operations and pillow for image manipulation regularly update dependencies keep your Python interpreter libraries and frameworks updated to ensure to address potential security vulnerabilities. The next section which says authentication and authorization the first subsection states best practice for user authentication in developing user authentication in developing user authentication in python applications, here are some best practices to be mindful of utilize when possible number one. Secure password management a. Strong password policies b. Secure hashing C. Salt generation d. Multifactor authentication MFA number two. Secure session management a. HTTPs measures enforce HTTPs for all communication to prevent interception of login credentials and other sensitive data. B. Secure session tokens use secure and unique session tokens instead of relying on cookies alone. C. Session timeouts use appropriate session timeouts to automatically log out inactive users, minimizing the risk of unauthorized access d. Secure token storage store session tokens securely on the server side never inclined site storage d. Number three impute validation and sanitization a. Validate and sanitize all user imputes, that is, prevent SQL injection, cosite scripting, and other attacks by carefully validating and sanitizing all user imputes, especially login credentials and search queries. B. Rate limiting implement rate limiting to prevent root force attacks and denial of service attempts. These are all worth mentioning. Secure number four secure coding practices a. Avoid hard coded credentials. Never store sensitive information like passwords or API keys directly on your code. Use environment variables or secure configuration files instead. B. Use secure libraries and frameworks. Utilize well maintained and secure libraries like flux login or the Django authentication system for user authentication functionalities c. Stay updated keep your Python interpreter libraries and frameworks updated to address potential security vulnerabilities. Number five additional considerations a. Use secure password. Best mechanisms allow users to reset their password safely through image verification or other secure methods. B. Log authentication attempts log login attempts, including successful unfield ones, for security monitoring and forensic analysis. C. Perform regular security audits conduct regular security audits of your application to identify and address potential vulnerabilities in your user authentication system. The next subsection says implementing secure authorization methods building for choices implementing secure authorization mechanisms I put like kind of team kind of thing so we have security. Your web application goes beyond strong authentication. It requires repost authorization mechanisms to control access and protect sensitive data. Here is roadmap to guide your implementation. Number one, HTTPs the essential shield encrypts all communication between users and applications using HTTPs. This prevents eavesdropping and ensures data integrity safeguarding safeguarding login credentials and user and other sensitive information. Number two strong authentication the gatekeeper choose a robust authentication method, ideally multifactor, to verify user identities effectively. Don't settle for weak passwords, enforce complexity, and regularly require password updates. Number three authorization defining access rules implement a granting authorization mechanism that defines who can access what. Utilize rule based access control, RBaC, or regular modules to assign permissions based on user roles and attributes. Session management vigilance guidance protect session management use secure tokens enforce timeout for inactive sessions and ensure proper storage on the server side never inclined side storage number five secure coding build with caution follow secure coding practices avoid hard coded credentials rigorously validate and sanitize user imputes and leverage well maintained security libraries. Remember, a single vulnerability can breach your entire defense update and monitoring. Number six update and monitor internal vigilance. Keep your web application frameworks and dependencies updated to patch vulnerabilities promptly conduct regular cybersecurity audits and monitor application activity for suspicious behavior practices. Defense is key. By implementing these steps, you build secure authorization methods mechanisms that protect your web application from unauthorized access, data breaches, and other threats. Remember, security is an ongoing journey, not a destination. Stay vigilant and adapt your defenses as the landscape evolves. Number four encryption and hashing in python over an overview of encryption and hashing techniques in the digital world, protecting data is paramount. Two key techniques used for this purpose are encryption and hashing. While goods transform data, they serve distinct purposes. Number one encryption reversible number one encryption encryption is reversible. Encrypted data can be decrypted back to its original form using a secret key. B confidentiality it protects data confidentiality by making it unreadable to anyone without the key. C. Common uses secure communication HTTPs storing sensitive data on digs protecting it files in transit popular algorithms involved popular algorithms involve AES, RSA, charch 20 the next one is hashing. Hashing is one is one way. It transforms data into a unique fixed length stream called a hash. B integrity it ensures data hasn't been tampered with. Any change in the data will result in different hash. C common uses password storage hashed not stored in plain text, verifying file integrity digital signatures d popular algorithms involved shall two, five, six, shard 2512 shall three and b trips. B trips are my personal favorite and the key differences in purpose encryption focuses on confidentiality, while hashic focuses on data integrity reversibility. Encrypted data can be decrypted, while hash data cannot be reversed. Computational cost hashing is generally faster than encryption using the right technique. The choice between encryption and hashing depends on your specific needs. You need to access original data, use encryption won't need to verify data integrity. Use hashing. Want to combine boots? Use salting with hashing for password storage. That is, add random string to the password before hashing, making it even harder to crack. Remember, both encryption and hashing are essentially essential tools for data security. Choose the right technique based on specific needs. Keep your eyes and embroidery secure. Now implementing encryption and hashing in python application. So let's go to code sample we have. So in this code sample, I would quickly change to my so first of all, we have our Python environments created. Let's change to our python environments to check. While it's doing that, let me create an encryption py. This is where we test our encryption. We'll have our hash hashing py. So let's quickly install. Let's also install cryptography. These are important it so let's start with encryption that is installed so we can get cryptography. This is our module import and it so let's start first of all by creating a cipher key, useful encryption so printers generates generates key. Then we have Sephora suites in our key, which I've created here. Now let's encrypt our message. So let's create a message set. This message invites. This is the confidential text received from NSA. Encrypted encrypt. So this way, we encrypt our message. Now let's decrypt this same message. Decrypt our message. Decrypted message. So we still call our cipher suits decrypt, passing our encrypted message. So in this case, we'll print our message just so. Here we print it. It. Encrypted message. Encrypted message. I've encrypted message here. For this we shall do decode to get it to print. Let's not forget to save the file. Yeah. So this is the confidential text from the NSA. Original message. Then we encrypted it. This is the encryption, and then we decrypted the text again. So it works very well. This is a very good method. So it lies in our python applications or python projects, in case you're working with. Working with a framework that doesn't. You don't understand the essay, you don't understand the encryption method or the hashing method. So you want to use your own. You can easily use this method, which has been shown here, to encrypt your information, and you can make it more, much more difficult depending on what you want to do with it. So now let's work on the hashing. So, to get our hashing working, we import hashlib. Let's import recrypt. Let's also import random. Now let's get our data to hash. Let's see this. Let's make the page data. This is data from my father. Now let's hash this sh two five six. Apologies. Using the shift button down there. So we call it the sh two five six hash, because in this case, we want to hash it using the sh two five six algorithm we'll call hashlib. I'll do sh two five six, and we'll pass our data in it. In here, we'll simply then say digest. Hex. Digest as to converts it. So this is our hash data, which will print very soon. Now let's hash with sha two five six. This time with salts. So this basically makes good hash data much more difficult to crack. So in this case, we created some random bits and give it to five six value there. Sh two five six hash. It's called sh two five six data. And here we simply just provide this to bytes to byte. This is currently in a string format, and we have a data in a string format. So that's in a byte. Format. So let's convert this to bytes and simply xhs. Now that is for just using the hash leap to hash. So now let's make use of let's hash with bcrypts. I mentioned Bcrypt because even in the jungle I'm going to look at my node applications. I still make sure to use decrypt because it really makes your passwords really complex, gives that complex feeling. So let's work with BcrypTW, most likely password pass our data, and let's pass bcrypt gen salt. Let's generate assault so you can actually add your own custom values there. But let's just use the default one provided by, let's call this shard two five six hash. Have this as shut five six hash and I will simply create a new line. Let me duplicate that. And this is big ribs hash. Call this bgrib with save this and let's run our code python. So as you can see, the bitcrypt looks a lot more complicated. We're having full stops, we're having dollar signs, we're having slash. On the other hand, Sha is just numerals, and I don't think I even see capital letters here. It's just a bunch of small letters and numbers. First one is just numbers. Second one is just numbers and letters too. But we get the point. So these are very good methods that we can use for creating hashing and encryption of messages, text, whatever information you want to secure. I hope that was a good example for you, and you really understood that. So now let's come back to our talk. So I've gone through implementing encryption and hashing in Python application. Let's now go to best practices for storage for secure storage of password and sensitive information protecting sensitive information, especially password discretion for any application. There are some clear some practices. Number one, never store passwords in plain text. Never do that. Use secure hashing with salting, just like I showed you. So verify password. Securely store hashes and salt securely utilize environment variables or secure configuration files to store hashes and salts not directly on your code. Consider utilizing password management systems for additional cybersecurity. There are other libraries you can leverage on for that, use secure frameworks and libraries. The Django and Flux offer builtin's passwords action mechanisms. Utilize libraries like passlive for password advanced password hashing features. Then the last one, encrypt sensitive data. For data beyond passwords, consider encryption libraries like Frenet or cryptography for secure storage and transmission. So number five, we have Python security tools and libraries. So number one, here we have introduction our first subsection, we have introduction to security focused libraries in Python. So in the ever evolving landscape security landscape, Python developers have access to rich ecosystem of libraries that empower secure coding practices. This note delves into the strength and use case of several prominent libraries offering guidance on how they can enhance the security of your Python application. So number one, we have cryptography. Core features include facility cryptography operations including symmetric inclusion, escape 20, asymmetric encryption, RSC, ECC hashing. Also sharp two, five, six on bitcrypt message authentication quotes. We have HMTC and digital signatures. The next one we have security use cases securely store and transmit sensitive data implements user authentication and authorization mechanisms digitally sign and verify files to ensure integrated integrity securely communicates over networks using TLS and SSM as transport layer security and secure socket layer. So we have Nmap. Core features include network discovery scanning, including port scans, os detection, service identification, viability detection limited, and packet sniffing. So use case security use case here is identify potential security vulnerabilities in your network infrastructure enumerate services running on remote hosts to access potential attack vendors. Gather network intelligence for penetration testing and security assessment. Then, number two, we have ScaPI. Core measures include network packet manipulation, crafting sniffing analysis, supporting various network layers and protocols, TCP IP, Internet security use, case develop and test custom network cybersecurity tools for intrusion detection, penetration testing, network forensics. This involves analyzing network traffic to identify suspicious activity of protocol deviation, crafting custom network packets, vulnerability protecting, and network force. Number four, we have core features. Number four, we have impact packets, a package that I really, really care about. The core features include interact with network protocols like SMB, file sharing, RPC, DcerPC, and active directory, primarily used for Windows systems. Cybersecurity use case includes automate penetration testing and variability assessment in Windows environments, perform credentialed attacks and post exploitation activities after gaining initial access, extract information from network shares and active directory from security analysis. So analysis and demonstration of security tools and integration of usage examples within I'll cover this integration and use examples within Python project, so I'll cover this while we have done almost done with most of the topic talk. So number section six emergent threats and future constraint okay, as we are coming closer to the end of it, what are some emergent threats and future constraint? Anticipating and mitigating emerging trades so, a rapid evolution in technology and the evergreen attack surface make anticipating and mitigating emerging threats crucial for any Python developer. This article here explores key strategies to stay ahead of the curve and measures robust security in your project number one involves identifying potential threats. Next under the sublist include staying informed. Regularly follow security news and updates on vulnerabilities affecting Python libraries, frameworks and dependencies. Subscribe to security advisories and mailing list of prominent Python projects. Another thing to do is to join Python communities. We have them on discord slack all around. I think also on Twitter we can find Python communities to join and get engaged. Understand attack vectors familiarize yourself common attack vectors targeting Python applications such as SQL injection, XSS code injection, and vulnerabilities in external libraries. Proactive threat modeling to conduct threats modeling exercises for your application to identify potential attack surfaces and vulnerabilities early in the development process. Mitigating risk that's number two, secure how to not mitigate risk now secure coding practices fully established secure coding practices like input validation, output encoding, proper error handling secure password management use libraries like cryptography for encryption and passlik for password hashing. Next one dependency management choose secure, well maintained libraries and frameworks regularly update your dependencies parts known vulnerabilities. Utilize tools like tree for dependencies, but visualization and vulnerability analysis okay, we serve secure configuration ensure the application configuration is secure evolving exposure of sensitive information use environments vulnerabilities for storing secret secrets instead of hardcoding them in code. Continuous monitoring implementing login and monitoring system to detect suspicious activity and potential incidents tools like elk stack or gridlock can be helpful to concentrate log management and analysis. Penetration testing conduct regular penetration testing of your application to identify and address exploitable vulnerabilities. Utilize tools like metasploits Kali Linux for penetration testing exercises. Always make sure to do things like this with the team so that you don't just be working alone and you get point of views of other people in case you're missing something. Staying ahead of the code. That's number two, staying agile and adaptable being prepared to adapt to security practices based on the latest threats and vulnerabilities. Emerging threats are constantly evolving, so continuous learning and adaptation are key. The next one says leverage community resources like I mentioned earlier, participate in community security, Python security community. Utilize forum mailing list and security conferences to stay informed, share knowledge, and learn from others experience. Last one says embrace security automation, automate security tasks and tasks like dependency scanning, vulnerability passion, and configuration management for improved efficiency and consistency. Remember, security is an ongoing process, not a one time fix. By adopting these strategies, you can practically anticipate, imagine threats, mitigate risk, and build more secure Python applications, which is the goal. So some additional tips to mention consider using secure coding frameworks frameworks like the Django Flags pyramid offer built in security features that can help protect against common vulnerabilities. Number two, educate your team. Make sure to get your team first of all, encourage your development team to follow secure coding practices and stay up to date on security threats. Utilize cloud services following up security threats a good mining service that can follow is the hacker news. It's worth mentioning, so they give quality and top notch news on the latest data breaches and hacking news generally. So the next one says it's lice. Cloud security services Cloud platforms like AWS, Azure and GCP offer various security features that can benefit your Python applications. So b future lookout for Python security practices and advancements here are some key points to consider. Growing security awareness okay, so the binary community is increasingly aware of security concerns, leading to a greater focus on security on secure coding practices, vulnerability management and threats mitigation strategies security focused libraries and frameworks like cryptography, passlib, and bandits are seen wider adoption and development take a course on YouTube take a course on coursera anywhere you can find probably udemy. There are lots of courses that talk about these frameworks and how they can be used properly, so you know how to utilize them in your Python applications and make more secure and more strategies. Python applications so educational resources and training programs on Python security are becoming more readily available. So just with the amount of money you can get these courses and get certified in them also and have the knowledge. So number two states advanced security tools and techniques static code analysis tools continued advancements in tools like bandits, flake aids, PY links will enable practices, identification and prevention of security vulnerabilities in code. The next one says AI power, threats, detection, machine learning and artificial intelligence will play a bigger role, analyzing code traffic, network traffic, and log data to detect and respond to security events in real time. Next one says quantum resistance cryptography as quantum computing evolves, any cryptographic algorithms and libraries will be developed to ensure the security of data and communication. The third one is integration with development workflows. Security will become more seamlessly integrated into development processes with tools and embedded practices embedded in CI CD continuous integration, continuous development pipelines, and automated testing frameworks. Security considerations will be evolving into all aspects of the development process, from design and architecture to coding and development. Developers will have access to better documentation, tutorials and training, and navigate security concerns and integrate them effectively. Focus on cloud security okay, this is the last one. With the increasing popularity of cloud based development and deployments, cloud security solutions specifically designed for Python applications will become more prevalent integration of Python security tools with cloud platforms, which practices and provide specialization management for security configurations. That's AWS, GCP, Azure and Digitalocean and the likes of that. So section seven, summary of key takebase. So summary here, I will just basically give you what's on my mind on the summary. So we've covered a bit of a lot of things. I talked about the best practices, look, the best practices, and how to make sure that let me just briefly come over to so I talked about the introduction to Python security, common security trends in python security practices in python authentication and authorization encryption and hashing in Python, which I gave a good sample, python security tools and libraries, emerging trends and future consideration. So all of these are really important information. With this knowledge and extra practice, you can gradually implement them in your Python application and Python development. And with strict adherence to some of these best practices, and many of these best practices, you can ensure to build more rigid and secure web applications, both web applications and Python applications generally. So, encouragement for implementing best practices and staying updated in security measurements. Well, I'll say one big encouragement is try not to get hacked because you're protecting fines in those companies that get hacked. That's a lot of money. So you save your company, you save your team a lot of money by implementing best practices and secure applications that are difficult to get hacked. Because most of the times we believe that, yeah, there's no way this can get hacked. And eventually something happens and hacker finds zero d. And yeah, it's done. So by focusing a lot on these best practices and securing our pattern applications as much as possible, we ensure that we are not vulnerabilities to this event, this unfortunate event. So that is a very big encouragement and also just to become better. That is a big encouragement. So I have had an amazing time with you. I guess I could not cover probably as much as I wanted to cover, but I guess I would also see, yeah, it's been an amazing time having this talk and I really hope you had a lot to learn from and you enjoyed it as much as I did. Thank you so much. Remember, my name is kanadin, you can contact me on LinkedIn. Yeah, whatever it is, we can talk about it. Thank you so much and goodbye.