AI risks: key cybersecurity measures

AI Risks and Preventive Measures

Artificial intelligence (AI) presents companies with immense opportunities to innovate products, streamline business models, enhance productivity, and reduce costs through automation. Cloud-based AI models from providers like Microsoft, Google, and Amazon have become integral to both business operations and everyday life. As AI development accelerates, its potential seems boundless, sustaining the enthusiasm surrounding its capabilities. 

However, alongside its opportunities, AI also introduces significant risks. These risks, if not addressed, can jeopardize the very existence of companies, particularly when cybercriminals exploit AI to execute attacks that compromise IT systems or extract sensitive data. Proactive risk mitigation is crucial, especially in critical infrastructure sectors such as energy, finance, healthcare, and public administration. Investments in AI-supported cybersecurity tools are becoming essential to counter these threats effectively.

Below, we explore key AI-related risks and outline preventive measures that IT managers can adopt to safeguard their organizations.

Key AI Cybersecurity Risks for Companies

1. Automated and Scalable Cyberattacks
   AI enables attackers to conduct cyberattacks with greater speed, precision, and scale. For instance, AI-powered algorithms can automatically identify and exploit vulnerabilities in networks and systems, leading to an increase in zero-day exploits or rapidly spreading phishing campaigns.
2. AI-Driven Phishing and Social Engineering
   Cybercriminals use AI to craft realistic phishing emails and social engineering schemes. With natural language processing (NLP), attackers can personalize their attempts by analyzing victims’ social media activity or corporate communications, making it harder to distinguish between legitimate and fraudulent messages.
3. Deepfake Technology for Fraud and Reputational Damage
   AI facilitates the creation of deepfakes—manipulated videos, audio, or images that appear authentic. These can be exploited for fraudulent activities, such as impersonating executives to initiate unauthorized transactions or disseminating false information to harm a company’s reputation.
4. AI-Based Malware and Ransomware
   AI-powered malware can adapt to bypass traditional security measures. Such malware dynamically modifies its strategies to evade detection and exploit complex vulnerabilities, posing challenges for existing IT security systems.
5. Attacks on AI Systems
   Organizations leveraging AI face risks of their systems becoming targets. Adversarial attacks, for example, involve feeding manipulated inputs to AI models, causing incorrect decisions or system malfunctions. In critical sectors like healthcare, energy, or finance, these attacks can have catastrophic outcomes.

Three Preventive Measures to Address AI Threats

1. Implement AI-Supported Cybersecurity Infrastructure Leveraging. AI in cybersecurity is a powerful way to counter AI-based threats. Intelligent security systems can: However, these systems require continuous monitoring and management by skilled IT professionals to ensure effectiveness.
   
   - Detect anomalies in network traffic and user behavior in real time.
   - Identify phishing and malicious content through behavior-based analysis.
   - Learn from past attacks and adapt to emerging threats dynamically.
2. Regular Employee Training. Employees remain the weakest link in cybersecurity. Regular training sessions are essential to raise awareness of potential threats. Key training topics include:Informed and vigilant employees play a critical role in preventing successful attacks.
   
   - Recognizing phishing emails and suspicious behavior.
   - Adopting secure practices, such as using strong passwords and multi-factor authentication (MFA).
   - Handling sensitive information cautiously, especially in response to unexpected requests.
3. Consistent IT Hygiene and Adversarial Testing. Maintaining robust IT hygiene is crucial. Regular assessments and updates help close security gaps. Recommended actions include:
   
   - Patching software and systems promptly.
   - Securing proprietary AI models against manipulation.
   - Simulating attack scenarios to test the resilience of AI systems and address vulnerabilities proactively.

Addressing Broader AI Risk Factors

1. Shadow IT and Cloud Sprawl
   Unapproved cloud-based AI tools can create security vulnerabilities. Centralized procurement and access control mechanisms are necessary to mitigate risks associated with shadow IT and cloud sprawl.
2. Data Security and Compliance
   Companies must ensure that sensitive data is adequately protected. Many organizations are transitioning critical data from the cloud back to on-premises systems to maintain control and comply with stringent security standards.
3. License Management
   Managing thousands of software licenses can become a security risk. Efficient license management reduces vulnerabilities and costs. Exploring the secondary software market offers additional benefits, such as cost savings and enhanced sustainability.

Benefit from PREDNY SLM Expertise in Used Software

PREDNY SLM, a leading provider of used software in Czechia for public sector, offers businesses and public institutions a wide range of cost-effective, legally compliant, and sustainable software solutions. Advantages of working with PREDNY SLM include:

• Savings of up to 70% on license costs compared to new versions.
• Transparent and audit-proof license acquisition processes via the SWTP portal.
• Expertise in large-scale IT infrastructure projects.
• Contribution to sustainability through active participation in the circular economy.

With PREDNY SLM’s extensive experience, companies can optimize their software management while enhancing security and sustainability.

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By understanding AI’s risks and implementing the outlined measures, companies can effectively guard against emerging threats and leverage AI responsibly to drive innovation and growth.