In the era of artificial intelligence, hackers are leveraging AI-driven techniques to breach even the most robust cyber protection programs. These AI-driven cyberattacks are reshaping the cybersecurity landscape, and it's crucial to adopt a comprehensive cyber protection program that onboards holistic defense. The connectors include common security concerns, ransomware, phishing or even privacy, and AI invokes some differences since it's being learned and revolutionized.
AI-driven cyberattacks involve using advanced machine learning algorithms to identify vulnerabilities, predict patterns and exploit weaknesses. The efficiency and rapid data analysis advance hackers' capability to gain a tactical advantage, leading to rapid intrusions or destruction. Traditional cybersecurity methods are no longer enough to combat sophisticated attacks since AI cyberattacks adapt and evolve in real time.
The traditional protection scheme for IT organizations during the early 2000s involved perimeter protection and malware issues. Organizations during those periods also focused on software security, but since software applications were at a minimum, external attack methods were a priority. Later, software applications surfaced to help resolve user-based productivity, and organizations built advanced perimeter protection devices such as intelligent firewalls, routers and switches to counter external network attacks.
Software and hardware attacks can pose a constant threat to businesses. However, there are effective ways to counter these threats. One such way is by utilizing a system dependency model. This model connects predictive analysis, response time, attack type, deterrence and cyber protection into a cohesive system rather than treating them as separate entities.
The system dependency model helps to predict attack patterns and counter intrusions, particularly for SOC personnel. Each team member is at an advantage due to the visual indicators and threat intelligence data provided by network security devices. However, AI cyberattacks require SOC personnel to reassess their cyber protection strategy.
Today's landscape operates differently since AI-driven cyberattacks are machine-invoked and adaptable to configuration changes. No cyber defenders can counter the real-time change, analysis and adaptability of AI-driven attacks. Since AI platforms utilize machine learning to determine the network behavioral patterns and soft targets, they can adapt and change their attack method.
Aside from adaptability and real-time analysis, AI-based cyberattacks also have the potential to cause more disruption within a small window. This stems from the way an incident response team operates and contains attacks. When AI-driven attacks occur, there is the potential to circumvent or hide traffic patterns. This is somewhat similar to criminal activity, where fingerprints are destroyed. Of course, the AI methodology is to change the system log analysis process or delete actionable data. Perhaps having advanced security algorithms that identify AI-based cyberattacks is the answer.
The U.S. Navy has used various principles and combat methodologies to deter and counter enemy engagements. One was situational awareness, which I discussed in my book The Cybersecurity Mindset. In my discussion, I emphasized that cyber and combat warfare have similar methodologies. One is learning tactics to counter an attack, which is why robust security algorithms are needed for AI-based cyberattacks.
AI has introduced challenges where security algorithms must become predictive, rapid and accurate. This reshapes cyber protection because organizations' infrastructure devices must support the methodologies. It's no longer a concern where network intrusions, malware and software applications are risk factors, but rather how AI transforms cyber protection. The shield is not broken. It requires a transformation practice for AI-based attacks.
The traditional IT landscape contains multiple risks relating to privacy, perimeter protection, software applications or data leakage. These risks are generally considered vulnerable since they introduce loopholes and weaken an organization's defense posture. The counter tactic is to remediate the risks and also increase cyber protection.
Invoking AI into the risk and vulnerability ecosystem transforms security compliance and cyber protection. Since AI utilizes behavioral analytics, machine learning and real-time analysis, enterprises must examine risks based on patterns and computational errors. This is where continuous monitoring and AI will operate best. Organizations must also determine how audits, assessments, configuration changes and remediation timelines should mature.
According to Built-In, 12 major risk areas affect AI operations, and privacy is the most severe. Knowing that the current compliance landscape excludes AI risks, how will risk frameworks and vulnerability remediation programs transform? In its nature, risks such as privacy leaks disrupt cyber protection and will make assessments, audits and remediation challenging. Just imagine a data leak within an AI platform. Although software-based, should the risk become categorized as software or AI-based? It is time to adjust those security controls!
Transforming cyber protection also requires control development and implementation. Typical frameworks such as NIST 800-53, CSF, ISO or OWASP are structured based on application, cloud, data, identity and infrastructure. So, should AI be implemented within its control framework, or should current controls be modified?
There are tradeoffs to implementing newer controls into an existing environment. One is that it requires a change process where security controls are added. This elevates additional work and may require an assessment. Typical control objectives relating to continuous monitoring may require a language change and state, 'Continuous monitoring must include AI software programs.'
The alternative to adjusting current controls is to have an inclusive category for AI-based systems. This would mean controls related to typical areas, such as access control, business continuity or software development, will be encapsulated under AI. This would also become challenging and labor-intensive. It also opens the possibility of a security gap where unnecessary controls are implemented or required controls are not covered.
Security transformation must include AI risks and newer protection schemes. All too often, security implementation and changes are an afterthought. The software development lifecycle (SDLC), continuous improvement and change management support security transformation. These tools should be utilized as best practices and resources to counter AI risks.
The future state of AI and cyber protection warrants a discussion to reduce additional enterprise and technology risks. Within the SDLC, numerous opportunities exist to determine whether AI-discovered risks can be managed and remediated. This sounds very simple, but are we there yet? We could examine the current protection scheme, including AI, and there's our answer.
Forbes Technology Council is an invitation-only community for world-class CIOs, CTOs and technology executives. Do I qualify?
