Cybersecurity attacks, including phishing, vishing, smishing, and everything else “ishing,” continues to rise and become even more of a problem for organizations and individuals.
The substantial annual damage caused by cyberattacks continues to rise for organizations and individuals. Unfortunately, smaller businesses are usually more vulnerable since they typically have worse systems to protect their data and information from criminals, overseas powers, etc., yet have data that these bad actors find desirable.
Artificial intelligence has become consumed in every facet of our lives, including autonomous cars, healthcare, content creation, and cybersecurity defense. Yet, this revolutionary here-and-now capability required extensive data to exist for AI to be effective.
How, if organizations go to great lengths to “protect and secure” their data, often suffering from data loss and breaches, how will this problem go away with an increase in AI becoming the lifeblood transformation of our time?
Dealing with the Need to “Feed” the Beast Called AI
AI becomes needed for many business use cases, including transformation customer success, cybersecurity XDR, deep learning techniques, and business analytics.
Without AI intelligent models, organizations cannot monetize their data. However, without data, we have no Artificial intelligence models. What happens to AI once more users move their data into a decentralized model with identity wallets linked to Blockchain platforms?
Who owns the data or may pull it into AI datasets?
As you are exploring AI for your organization, ask the hard questions early:
- Where does AI pull its data?
- Does this create a new security attack surface for my organization?
With Blockchain networks and Web 3.0 on the horizon as the next great digital transformation, is the decentralization of data and blockchain identity the savior for data loss and prevention? Suppose individuals and organizations own the right their place their data anywhere within the decentralized realm along with holding the permissions. What role will AI play in these engines, and will algorithms function without data access?
Fortifying Data Behind the Blockchain Technologies
The Blockchain security model is a significant leap in the progress of transactional protection. However, the process of Blockchain is limited in the number of transactions. How will an organization live in a world of data retention to feed the AI models to gain positive outcomes while coping with next-generation cyber-attacks?
Should organizations abandon their plans for AI and machine learning techniques once they have mastered data management and security? The two factors to come into AI conversation and data; are cost and risk.
What is the cost to keep the data in the cloud long enough to gain some monetization with AI? The data storage and access cost continue to rise even with cloud service providers entering the market.
What is becoming the higher cost of data management? Of course, it is security. How much will it cost the organization to protect its data across a multi-cloud infrastructure long enough to survive countless data breaches for the AI modeling and learning to be adequate to feed into machine learning?
Cost is a factor in AI modeling. Not only of the cost of data management and security a factor, but the cost of data science and analytics is also another core component affecting the ROI model.
Yes, with firms like Snowflake and Databricks, organizations can leverage cloud-based platforms for data lakes and analytics. However, this also comes at a cost.
Where Does Risk Fall into the Conversation?
Risk is still a “4-letter word, especially for regulated industries.” On par with other “4-letter words,” like cost, these words are always top of mind for CEOs. What is the risk of hosting so much data to feed into an AI model long enough to gain some optimization for customer retention, product development, competitive analysis, and predictive analytics?
If organizations review AI as strategic to their business models, having to “hold on” to data is a security risk. Ultimately, cybersecurity continues to take a backseat to business optimization while the promise of AI and ML continues to make headlines.
BTW: — Cybersecurity breaches also make headlines, too.
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