You're working with new technology in cybersecurity. What mistakes are you making?

We only know a drop in the ocean, You need to be proactive, curious and hunger to learn Integrity is very very important Invest on automation that saves time and cost

Adapt or die. Understanding the complexities of AI systems and the data supply chains that power them is vital to effectively protecting them. Many information security practitioners - even in 2024 - still operate from dated mental models, though. For example, the term "on prem" is still quite common in security circles when 99% of the time it is (incorrectly) used to describe Infrastructure-as-a-Service (IaaS) deployments. These conceptual misunderstandings can translate directly into flawed threat and security models. The best solution is continuous, hands-on experimentation with new technologies. Examples include: - leveraging the OpenAI API - fine-tuning open source models - building AI data pipelines for personal projects

Failing to update skills with evolving tech leads to gaps in knowledge, reducing effectiveness against new threats. Stay current through continuous learning and training.

"You're so deep in cybersecurity, you accidentally secure your lunch instead of your data!" In my exploration of cutting-edge cybersecurity technology, I sometimes overlook the importance of thorough testing and fail to anticipate potential vulnerabilities. However, I'm learning to embrace these mistakes as valuable lessons, continuously refining my approach to ensure robust protection against evolving threats.

Never feel that you know everything in Cybersecurity. The second you get the attitude that you are now a master of all tools and skills is when you will get hit with the reality of a data breach. Always be upskilling and reskilling in Cybersecurity Find new job postings in your cybersecurity niche and find out what skills or cybersecurity certifications you are missing. Use those to find out what your learning path should be for the year

One critical mistake that organizations often make when adopting new cybersecurity technology is failing to conduct thorough testing before deployment. Rushing into implementation without adequately testing the solution can have severe consequences, as it increases the likelihood of overlooking critical vulnerabilities or misconfigurations that could be exploited by adversaries. For sure testing in a new landscape needs extra preparation: new risks, new situations, new scenarios.. a lot of what if questions to consider. Creative thinking and a comprehensive knowledge of the details is needed.

?? Saltarse las pruebas en tecnologias de ciberseguridad es como cerrar los ojos y cruzar la autopista: peligroso y no recomendable. Antes de desplegar nuevas tecnologías de ciberseguridad, asegúrate de probarlas a fondo. Si no, podrías estar invitando a ciberataques a una fiesta sorpresa en tu red. ?Haz las pruebas o prepárate para sorpresas! ???? #CiberPruebas #DevSecOps"

The implementation of new technology in cybersecurity holds promise for enhancing security measures, but it also comes with inherent risks if not thoroughly tested. Neglecting comprehensive testing may result in unforeseen errors or vulnerabilities, jeopardizing security and performance. Moreover, it can lead to wasted resources on ineffective solutions. To mitigate these risks, it's imperative to adhere to a robust testing protocol, employing suitable methods and metrics to assess solutions thoroughly. Engaging stakeholders, users, and clients in the testing phase enables valuable feedback and ensures alignment with their needs and expectations.

New technology like artificial intelligence has a massive appetite for data. Combined with the complexity of modern supply chains, it becomes extremely challenging to map and secure your information effectively. Protecting it requires a comprehensive governance framework that applies the basics - preserving confidentiality, integrity, and availability - while acknowledging the unique challenges that AI presents. This includes vulnerabilities like: - Sensitive data generation - Unintended training - Prompt injection Laying a foundation of effective policies and procedures, and building on top of it with technical controls, is the best way forward.

Neglecting data security exposes sensitive information to breaches. Encrypt data, control access, and regularly back up to mitigate risks.

The utilization of new technology in cybersecurity presents opportunities for leveraging large volumes of data for analysis and decision-making. However, it also introduces risks associated with data exposure, theft, or manipulation. Failing to secure data adequately can lead to violations of privacy, compliance, or ethical standards, as well as reputational damage. To mitigate these risks, it's crucial to adhere to the data security lifecycle, implementing encryption, authentication, authorization, backup, and recovery measures. Additionally, monitoring data activities and promptly reporting any incidents or breaches are essential practices for maintaining data integrity and safeguarding against potential threats.

Resisting change in cybersecurity practices can leave defenses outdated. Embrace innovation and adapt strategies to counter evolving threats efficiently.

"But we've always done things this way" should be a big red flag, especially when it comes to cybersecurity. Change may be uncomfortable sometimes, but it is necessary.

Adapting to new technology in cybersecurity is crucial for staying competitive and relevant in today's dynamic landscape. Failure to embrace change may result in losing your edge and facing dissatisfaction from stakeholders. To mitigate this risk, it's essential to view change as an opportunity, communicate openly with stakeholders, and remain flexible and agile in your approach. By listening to feedback and adjusting plans accordingly, you can effectively navigate evolving expectations and maintain your position in the market.

In the case of artificial intelligence, best practices are still evolving, which makes it difficult to follow them consistently! For example, hyper-indexing on certain risks, such as unintended training while using Software-as-a-Service (SaaS) applications, might appear to be a best practice. But it is important to balance this against the risk of misconfiguration and other challenges associated with Infrastructure-as-a-Service (IaaS) deployments. There is rarely a "right answer," and best practices will usually depend on your risk appetite, business requirements, and compliance concerns.

There are so many fundamentals to consider with any new technology, but specifically with AI in mind Data Flow Enforcement stands out to me. Most organizations are already ignoring these controls, their impact increases exponentially when AI is in the mix.

Embracing new technology in cybersecurity brings exciting opportunities, but it also comes with its share of challenges. Without adhering to best practices, there's a risk of compromising security and efficiency. To mitigate these risks, it's essential to follow industry standards, document processes, and regularly review and update best practices. By fostering a culture of continuous improvement and learning from both successes and failures, organizations can navigate the complexities of cybersecurity technology effectively.

Tools are just tools. Cybersecurity is very tool-centric, and what we, from my perspective, often miss is a more use-case-centered focus. Instead of immediately asking which tools we need to have in a strategy, we need to ask what the problems are and how we will address them. Only once this is clear can we set out to find the correct tool for the job. Following this, we then need to think about the implementation: I have witnessed way too many implementations falling short of their goal because they were 70% complete when the focus suddenly shifted to the next "even better" new technology or tool. So, let us focus on the process: Problem statement - solution - tool to support the solution - implementation of the tool and support process

Embracing new cybersecurity technologies comes with its set of challenges. Key missteps include lacking executive buy-in, underestimating human error, and selecting complex tools without proper team training. Outdated policies and overlooked software updates can leave systems vulnerable. Additionally, mishandling data migration and skimping on employee training can expose organizations to risks. Relying solely on antivirus solutions is no longer sufficient against evolving threats. A deep understanding of the organization's risk profile and a comprehensive, updated cybersecurity strategy are crucial for effective defense. Avoid these common pitfalls to strengthen your cybersecurity posture.

“Any sufficiently advanced technology is indistinguishable from magic.” Arthur C. Clarke might not have been talking about AI when he said this, but it's applicable to how many people - including security, privacy, and compliance professionals - are treating new advancements. ?? The tried and true concepts of data confidentiality, integrity, and availability remain relevant. ?? On the privacy side, there remains gray area when, but there are practices you can implement to reduce your risk regardless of what regulators decide. ?? With compliance frameworks (SOC 2, ISO 27001, etc.), being able to confidently explain what you are doing and why will be crucial when auditors aren’t sure how AI-enabled operations should be running.