This is why training and upgrading

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Let’s not forget about the costs. A company seeking to provide adequate protection against cybercriminals requires a lot of effort and investment. Developing and operating AI systems in cybersecurity can require significant resources and computing power, which can be financially and technically challenging for some organizations. Implementing AI systems into existing infrastructure and business processes can be complex and require reviewing and upgrading current systems and procedures. In addition, we may face problems with interpretability and explainability. Many AI models, especially deep neural networks, can be complex and difficult to understand. This creates new challenges in explaining how exactly the model makes decisions, which is important for trust and compliance. machine learning models requires ongoing investment in financial and intellectual resources.

The answer to these challenges can only be a comprehensive approach, including continuous monitoring and updating of systems, training of personnel and compliance with regulations. Cybersecurity using AI requires constant attention and investment to effectively cope with changing cyber threats. We will strengthen fences, put up new locks and train dogs as long as we want to live in relative safety. And by the way, artificial intelligence will also tell us what kind of fence to build right now - having analyzed potential threats and calculated a picture of the future.

Forrester: What Place Will Generative AI Take in denmark mobile database Tools?
25.10.2023
Allie Mellen, principal analyst at Forrester , shares key findings from a new study, “ How Security Tools Will Leverage Generative AI, ” in a corporate blog post that examines how generative AI will impact six different areas of security: detection and response, zero trust, security leadership, product security, privacy and data protection, and risk and compliance.

There’s a lot of buzz around generative AI, but it’s still a bit of a mystery to many. According to Forrester, AI decision makers believe that IT operations will be the most impacted by generative AI in the enterprise, including security. Security leaders should be prepared for the impact this new technology will have on their teams.

Below are five key areas where generative AI can be applied in security applications.

1. Generative AI is not ready for use yet
Despite press releases from vendors, this technology is currently only available to select customers, if at all. Every vendor we spoke to at least posted a teaser for a generative AI offering they were developing, but none are publicly available and likely won’t be until the first half of 2024.

2. Three main use cases for generative AI
In the area of ​​security they are as follows:

Content creation. Generating various forms of content (e.g. text, code, etc.).
Predicting behavior. Predicting the next step in a behavior pattern.
Articulation of knowledge. Presentation of information in a form more convenient for a person.

These scenarios can be applied differently within each security domain (threat detection, zero trust, etc.) and more broadly beyond that domain. Grouping generative AI applications by use case helps to consider the added value of each tool, how it is implemented, and how it uses input data.

3. Generative AI, when done right, can greatly improve the analyst experience