AI Security in 2024: Defense Against AI-powered Cyberattacks
Businesses can implement AI-powered security solutions into their systems to protect against online & offline security issues. Though AI is an effective solution to protect organizations from cyberattacks, it also enables attackers to launch complex, automated attacks.
Another aspect of AI security is the security of machine learning systems powering decision making of companies and autonomous systems. It has been proven that simple changes in inputs can cause these systems to fail, enabling attackers another attack surface. Therefore, companies need to consider security when implementing AI solutions.
What is AI Security?
AI is shaping multiple aspects of security. Here we explain all aspects of AI security. However, the rest of the article will focus on AI in cybersecurity as this is the most common AI application in the security field today.
AI in cybersecurity
AI both presents opportunities for information/cybersecurity professionals to improve their cyber defenses and new threats as cyber attackers leverage modern, publicly available machine learning algorithms.
Using AI to improve cybersecurity
Organizations leverage artificial intelligence to enhance their security against cyberattacks such as malware, phishing, network anomalies, and unauthorized access to sensitive data. These tools use machine learning algorithms to learn from historical data and detect anomalies to enable organizations to prevent and manage cyberattacks effectively and efficiently. For example, AI powered deception technology helps delay and identify cyber attackers.
AI can also be used for data loss prevention. For more on AI-powered DLP:
- Top AI DLP best practices to safeguard your business data.
- Top automated DLP software on the market.
Defending against AI driven cyber attacks
>90% of cybersecurity professionals in the US and Japan anticipate malicious AI-powered attacks. This is because AI research is publicly available and it can be used to build intelligent, continuously learning exploits by attackers.
Alejandro Correa Bahnsen, Cyxtera’s vice president of research, states:
An average phishing attacker will bypass an AI-based detection system 0.3% of the time, but by using AI this ‘attacker’ was able to bypass the system >15% of the time
For example, deepfakes are highly realistic videos, audio recordings, or photos generated by AI techniques. Some of their potential malicious uses include:
- Overcoming biometric security systems
- Infiltrating social networks
- Using realistic video/audio/photos for manipulating users and gaining access to corporate networks/information
AI-Powered physical security systems
Cameras record and transfer data to image recognition systems to identify threats (e.g. trespasser identification with cameras).
Securing AI systems against adversarial attacks
With AI technology, organizations have new processes such as data ingestion, preparation and labeling, model training, inference validation, and production deployment. These processes are new layers added to the organization’s tech processes that need to be protected from adversarial attacks. In adversarial attacks, attackers change the inputs of machine learning models to cause the model to make mistakes.
Since few deep learning systems are currently in production, adversarial attacks are still a mostly theoretical threat. Once deep learning systems start making important decisions, the importance of these threats will increase significantly. For example,
- autonomous driving systems can be manipulated with subtle changes to road signs or their surroundings
- industrial automation systems can similarly be manipulated for industrial sabotage
Why is it important now?
As an organization collects more data from different resources, potential points of cyberattack increases. According to a survey by Capgemini Research Institute, 69% of enterprises believe AI is necessary for cybersecurity due to the increasing amount of threats that cybersecurity analysts can handle. Survey results show that 56% of the firms say their cybersecurity analysts are overwhelmed and 23% are not able to detect all breaches.
With all these investment opportunities, the AI security market is forecasted to reach USD 38 billion by 2026 from USD 8 billion in 2019, at a CAGR of 23.3%
What are its use cases and leading companies for these use cases?
E-mail monitoring: E-mail is a common target for cyber threats. AI monitoring software helps improve the detection accuracy and the speed of identifying cyber threats.
- Tessian
Network threat analysis and Malware Detection: Organizations use AI to identify malicious malware and the differences between real and artificial users to prevent fraud access.
- LogRhythm
- SparkCognition
- Cylance
- White Ops
- Versive
- Cybereason
- Cylance
- Anomali
- Fortinet
- Palo Alto Networks
- Shape Security
- Cujo AI
AI against AI-based threats: Hackers are using AI as well. Organizations need AI to prevent an organization from AI-based threats.
- Check Point
AI to automate repetitive security tasks: Organizations leverage AI to automate repetitive tasks of security analysts so that they can shift their focus on more important tasks.
- Vectra
Fortinet and Palo Alto Networks are the two leading AI security companies that generate 1.8 billion USD and 2.27 billion USD respectively.
Further Reading
If you want to improve the security of your organization but don’t know where to start, here are a few pieces of our research about cybersecurity:
If you believe your business can utilize AI to improve security, you can go through our lists of AI platforms, consultants and companies before deciding.
If you still have unanswered questions, please feel free to contact us:
Sources
Cem has been the principal analyst at AIMultiple since 2017. AIMultiple informs hundreds of thousands of businesses (as per similarWeb) including 60% of Fortune 500 every month.
Cem's work has been cited by leading global publications including Business Insider, Forbes, Washington Post, global firms like Deloitte, HPE, NGOs like World Economic Forum and supranational organizations like European Commission. You can see more reputable companies and media that referenced AIMultiple.
Throughout his career, Cem served as a tech consultant, tech buyer and tech entrepreneur. He advised businesses on their enterprise software, automation, cloud, AI / ML and other technology related decisions at McKinsey & Company and Altman Solon for more than a decade. He also published a McKinsey report on digitalization.
He led technology strategy and procurement of a telco while reporting to the CEO. He has also led commercial growth of deep tech company Hypatos that reached a 7 digit annual recurring revenue and a 9 digit valuation from 0 within 2 years. Cem's work in Hypatos was covered by leading technology publications like TechCrunch and Business Insider.
Cem regularly speaks at international technology conferences. He graduated from Bogazici University as a computer engineer and holds an MBA from Columbia Business School.
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