Cybersecurity attacks, including phishing, vishing, smishing, and all other “issings,” continue to grow and become an even bigger problem for organizations and individuals.
The amount of annual damage suffered by organizations and individuals from cyberattacks continues to increase. Unfortunately, small businesses typically have poor systems to protect their data and information from criminals, foreign powers, etc., yet they have data that these bad actors find desirable. more vulnerable.
Artificial intelligence is being consumed in every aspect of our lives, including self-driving cars, healthcare, content creation, and cybersecurity defense. But this revolutionary “here and now” capability required the vast amount of data that existed for AI to be effective.
If organizations go to great lengths to “protect and secure” their data, and often suffer from data loss or breach, how will this problem be resolved and the rise of AI will become the lifeblood of our time? Will it be a transformation?
AI is required for many business use cases, including transformational customer success, cybersecurity XDR, deep learning techniques, and business analytics.
Without AI intelligent models, organizations cannot monetize their data. However, without data, there is no artificial intelligence model. What happens to AI when users migrate their data to a decentralized model with identity wallets linked to blockchain platforms?
Who owns the data or who might ingest it into AI datasets?
If you’re considering AI for your organization, ask the hard questions early on.
- Where does AI draw data from?
- Does this create a new security attack surface for your organization?
With blockchain networks and Web 3.0 on the horizon as the next big digital transformation, will the decentralization of data and blockchain identities be the savior of data loss and prevention? while owning the right to place the data anywhere within the distribution area. What role will AI play in these engines, and will the algorithms work without data access?
The blockchain security model is a giant leap in the advancement of transaction protection. However, blockchain processing has a limit on the number of transactions. How will organizations live in a world of data retention to feed AI models for positive outcomes while dealing with the next generation of cyberattacks?
Should organizations abandon plans for AI and machine learning techniques after mastering data management and security? Two factors in AI conversations and data. cost and risk.
What is the cost of keeping data in the cloud long enough to be monetized with AI? Data storage and access costs continue to rise as cloud service providers enter the market.
What makes data management expensive? Security, of course. How much will it cost your organization to protect your data across your multi-cloud infrastructure, withstand countless data breaches, and feed AI modeling and learning sufficiently into machine learning?
Cost is a factor in AI modeling. Data science and analytics costs, as well as data management and security costs, are another important factor that influences the ROI model.
Yes, companies like Snowflake and Databricks allow organizations to leverage cloud-based platforms for data lakes and analytics. However, this comes at a price.
Risk is still “a four-letter word, especially for regulated industries.” Like other “four-letter words” like cost, these words are always on the mind of CEOs. What are the risks of hosting large amounts of data feeding AI models long enough to optimize customer retention, product development, competitive analysis, and predictive analytics?
If an organization sees AI as strategic to its business model, having to “hold” data becomes a security risk. Ultimately, while the possibilities of AI and ML continue to make headlines, cybersecurity continues to lag behind business optimization.
BTW: — Cybersecurity breaches also make headlines.
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