<\/p>\n
- \n
- 78\u202f% of companies surveyed globally<\/a> already use AI or plan to deploy it<\/li>\n
- Companies that combine security automation with AI reduce the average cost per data breach by $2.2 million<\/a><\/li>\n
- In June\u202f2024 alone, the AI Incident Database recorded 37 new types of harm<\/a> – from deepfakes to algorithmic bias<\/li>\n<\/ul>\n
<\/p>\n
In short: More and more people in organizations are using AI, and it demonstrably makes many things safer – but can also cause harm in increasingly diverse ways.<\/p>\n
In this article, we\u2019ll examine two perspectives that will hopefully give you a solid initial understanding of AI security in the enterprise.<\/p>\n
Two Knowledge Traps That Can Catch Anyone (Including You!)<\/h2>\n
We\u2019ve all experienced this scenario: You use an AI, rejoice over fast results – and easily overlook what\u2019s happening under the hood. It\u2019s precisely in these moments that critical errors can slip through unnoticed. As the old saying goes: Ignorance is no excuse.<\/em> Two concrete examples illustrate how a lack of AI knowledge can lead to serious problems:<\/p>\n
1. The Glossy Label as the Deciding Factor<\/strong><\/h3>\n
\u201cAgentic AI\u201d – AI that autonomously handles complex tasks – is hailed as the buzzword of 2025. No surprise there: Within five years, AI systems have evolved from tasks taking one second to activities<\/a> that would take a human a full hour today. Sounds impressive. What\u2019s often left unsaid: This miracle AI doesn\u2019t deliver reliably. According to one of the most common evaluation methods, it completes its assigned tasks only about half the time – yet this very performance is marketed as success.<\/p>\n
An employee who finishes only 50\u202f% of their work wouldn\u2019t get a bonus – they\u2019d get a corrective talk with HR.<\/p>\n
The problem lies in the benchmark: AI is often evaluated using \u201cpass@k<\/a>\u201d (\u201cDid it work at least once in k attempts?\u201d). Great for prototypes, often catastrophic in production. Truly operational AI requires \u201cpass^k\u201d – consistent success on every single run. Sure, AI will likely soon achieve 99\u202f%+ reliability instead of today\u2019s ~50\u202f%. But right now – especially with agents – we\u2019re often still playing roulette.<\/p>\n
So pay attention: Test for real reliability, not lucky hits.<\/p>\n
2. The RAG Illusion: \u201cMy PDFs Don\u2019t Lie\u2026\u201d<\/strong><\/h3>\n
Retrieval-Augmented Generation (RAG) sounds like a guarantee for accurate answers – after all, the information comes straight from your own documents. But reality is trickier: The AI model must fit everything important into a limited context window – and that doesn\u2019t always work. The \u201cNeedle in a Haystack<\/a>\u201d test shows, for example, that even when the correct sentence is present, the AI can completely miss it – especially with long contexts or poorly placed information. Seemingly irrelevant prompt details can suddenly make the difference between a 27\u202f% and a 98\u202f% accuracy rate. So an LLM can have the correct paragraph right in front of it – and still be entirely wrong. Again: Trust is good – structured prompting and targeted evaluation are better. And essential whenever money or lives are at stake.<\/p>\n
This example shows how dangerous missing knowledge in the wrong place can be – similar to the misconception that a fork and an electrical outlet make a good combination. And just like with that literally shocking example, even a minimal understanding of AI helps avoid major blunders. Which brings us to the second category:<\/p>\n
Damage Scenarios Better Avoided<\/h2>\n
Ignorance is one thing – deliberate external attacks are another. AI systems offer a number of potential attack vectors<\/a>. We won\u2019t dissect all of them here; instead, we aim to raise awareness of their potential so you can make informed decisions. Let\u2019s look at two examples:<\/p>\n
1. Deepfake Call to the CEO<\/strong><\/h3>\n
![\"Mehr](\"https:\/\/www.cloudmagazin.com\/wp-content\/uploads\/2025\/09\/Autonome-Agenten-fuehren-zu-groesserer-Angriffsflaeche-250x167.png\")
<\/a><\/p>\nGreater autonomy means a larger attack surface – a challenge for AI security. Image source: Benjamin Eidam.<\/p>\n<\/div>\n
With just three seconds of original audio<\/a>, tools like Microsoft\u202fVALL\u2011E can clone a voice indistinguishable from the real one. Compounding the risk, studies show people detect such audio forgeries only 30-50\u202f% of the time<\/a>, depending on context. In July\u202f2025, even U.S. Secretary of State Marco\u202fRubio fell victim<\/a> to a scammer who used an AI voice imitator to impersonate Rubio in calls to several colleagues. A single forged approval call is enough to move multiple millions<\/a>. A possible preventive measure: Personal \u201ccatchphrases\u201d that quickly and clearly confirm the caller\u2019s identity. Example: My first hamster Manfred had eyes that sparkled like diamonds.<\/em> Or even simple, conversation-specific \u201cpasswords\u201d like broccoli casserole<\/em> (unless you\u2019re talking to a vegetable vendor). Agree in advance, practice briefly – and gain audio deepfake protection.<\/p>\n
2. Autonomous Agents, Autonomous Errors<\/strong><\/h3>\n
Every additional step an agent can take unsupervised dramatically multiplies the attack surface<\/a>. Problems can arise not only in writing but also during autonomous web research<\/a>, interaction with other software, or integration of results into internal systems – and in many other situations where an agent acts independently. In practice, this means: Agentic systems can work – and often impressively well, as Genspark<\/a> demonstrates. But the more autonomous a system becomes, the more thoroughly, deeply, and repeatedly it must be tested – again and again. Otherwise, you\u2019re building Russian roulette into your software.<\/p>\n
\n\n$2.2 million<\/div>\nCompanies that combine security automation with AI reduce the average cost per data breach by $2.2 million.<\/div>\n<\/div>\n\n78\u202f%<\/div>\n78% of companies surveyed globally already use AI or plan to deploy it.<\/div>\n<\/div>\n\n50\u202f%<\/div>\nYet this very performance is marketed as success. An employee who completes only 50% of their work\u2026<\/div>\n<\/div>\n<\/div>\n\n \n”<\/span><\/p>\n
\/\/ Key point<\/div>\nAs the old saying goes: Ignorance is no excuse.<\/p>\n<\/blockquote>\n<\/figure>\n
What to Do From Here?<\/strong><\/h2>\n
Simply recognizing that AI security isn\u2019t a niche topic – but carries very real risks – is already half the battle: the much-cited awareness<\/em>. The other half? Acting – thoughtfully, not out of panic.<\/p>\n
Those who understand why this matters (because otherwise sensitive data, budgets, or entire business processes are at risk) can respond strategically: question processes, secure systems, sensitize teams – and above all, invest continuously in education.<\/p>\n
This way, you can adapt your daily work to safely harness AI\u2019s opportunities without taking unnecessary risks.<\/p>\n
<\/p>\n
<\/p>\n
Header Image Source: Benjamin Eidam<\/em><\/p>\n
Read more on cloudmagazin.com<\/h3>\n
- \n
- DevSecOps: Security as an integral part of cloud development<\/a><\/li>\n
- Federated AI approach instead of one-size-fits-all models: Zoom and NVIDIA restructure enterprise AI deployment<\/a><\/li>\n
- IT security spending in Germany grows by double digits<\/a><\/li>\n<\/ul>\n
More on this topic:<\/strong> Additional articles on mybusinessfuture<\/a><\/p>\n","protected":false},"excerpt":{"rendered":"Imagine your company delegates half of all routine tasks to an Artificial Intelligence (AI) tomorrow. Tempting \u2013 until a chatbot accidentally publishes confidential contracts or an autonomous agent independently deletes a test database. Whether AI accelerates your growth or becomes a boomerang depends on your AI security, as Benjamin Eidam, AI\u2026 » Article<\/a>","protected":false},"author":98,"featured_media":26556,"comment_status":"closed","ping_status":"closed","sticky":false,"template":"","format":"standard","meta":{"_acf_changed":false,"_yoast_wpseo_meta-robots-noindex":"","_yoast_wpseo_meta-robots-nofollow":"","_yoast_wpseo_meta-robots-adv":"","_yoast_wpseo_canonical":"","_yoast_wpseo_opengraph-title":"","_yoast_wpseo_opengraph-description":"","_yoast_wpseo_opengraph-image":"","_yoast_wpseo_opengraph-image-id":0,"_yoast_wpseo_twitter-title":"","_yoast_wpseo_twitter-description":"","_yoast_wpseo_twitter-image":"","_yoast_wpseo_twitter-image-id":0,"pre_headline":"","bildquelle":"","teasertext":"","language":"de","_evm_translation_lang":"","featured_post":0,"featured_post_sortierung":0,"_wp_old_slug":["alignment-instead-of-nightmare-how-ai-security-becomes-a-success-factor-2"],"footnotes":""},"categories":[923],"tags":[],"industry":[],"class_list":["post-30905","post","type-post","status-publish","format-standard","has-post-thumbnail","hentry","category-expert-opinions"],"evm_reading_time_minutes":7,"wpml_language":"en","wpml_translation_of":26551,"acf":[],"yoast_head":"\n
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He oversees the strategic direction of the magazine and the entire MBF Media network, comprising four B2B trade magazines for IT decision-makers in the DACH region.","sameAs":["https:\/\/www.linkedin.com\/in\/tobias-massow\/"],"url":"https:\/\/www.cloudmagazin.com\/en\/author\/tobias\/"}]}},"_links":{"self":[{"href":"https:\/\/www.cloudmagazin.com\/en\/wp-json\/wp\/v2\/posts\/30905","targetHints":{"allow":["GET"]}}],"collection":[{"href":"https:\/\/www.cloudmagazin.com\/en\/wp-json\/wp\/v2\/posts"}],"about":[{"href":"https:\/\/www.cloudmagazin.com\/en\/wp-json\/wp\/v2\/types\/post"}],"author":[{"embeddable":true,"href":"https:\/\/www.cloudmagazin.com\/en\/wp-json\/wp\/v2\/users\/98"}],"replies":[{"embeddable":true,"href":"https:\/\/www.cloudmagazin.com\/en\/wp-json\/wp\/v2\/comments?post=30905"}],"version-history":[{"count":10,"href":"https:\/\/www.cloudmagazin.com\/en\/wp-json\/wp\/v2\/posts\/30905\/revisions"}],"predecessor-version":[{"id":48918,"href":"https:\/\/www.cloudmagazin.com\/en\/wp-json\/wp\/v2\/posts\/30905\/revisions\/48918"}],"wp:featuredmedia":[{"embeddable":true,"href":"https:\/\/www.cloudmagazin.com\/en\/wp-json\/wp\/v2\/media\/26556"}],"wp:attachment":[{"href":"https:\/\/www.cloudmagazin.com\/en\/wp-json\/wp\/v2\/media?parent=30905"}],"wp:term":[{"taxonomy":"category","embeddable":true,"href":"https:\/\/www.cloudmagazin.com\/en\/wp-json\/wp\/v2\/categories?post=30905"},{"taxonomy":"post_tag","embeddable":true,"href":"https:\/\/www.cloudmagazin.com\/en\/wp-json\/wp\/v2\/tags?post=30905"},{"taxonomy":"industry","embeddable":true,"href":"https:\/\/www.cloudmagazin.com\/en\/wp-json\/wp\/v2\/industry?post=30905"}],"curies":[{"name":"wp","href":"https:\/\/api.w.org\/{rel}","templated":true}]}} - Federated AI approach instead of one-size-fits-all models: Zoom and NVIDIA restructure enterprise AI deployment<\/a><\/li>\n
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