We cannot avoid the increase of machine learning (ML) and AI, which will boost every aspect of our digital lives, both good and bad. It is already occurring, with GovTech estimating that 17% of all cyberattacks will involve generative AI by 2027.
However, cybersecurity is growing to meet this demand, and we are seeing a rise in "fighting fire with fire," with ML in the hands of both security and hackers. So, what does this security revolution mean for our digital defense mechanisms in the coming years?
Keep reading to explore how machine learning in cybersecurity boosts areas such as:
- Real-time security analysis
- Predictive threat detection
- Cloud data protection techniques
- Insider threat mitigation
- Automated compliance monitoring
Discover how CMMC standards in 2025 will lay the groundwork for a more secure future in the battle against ML, and learn the steps you can take to leverage AI in your cybersecurity strategy moving forward.
The Role of Machine Learning in Cybersecurity
Businesses are already champing at the bit to use ML in their cybersecurity efforts, with CSO Online reporting that almost 90% of cybersecurity leaders have a vested interest in using AI to boost security in some form. Many businesses recognize the dangers AI poses and thus want to secure themselves against the possibilities while traditional cybersecurity measures fall behind.
ML offers a more dynamic adaptability, allowing it to respond at any time to any issue it detects. Its responsiveness is not in the here and now, either, and ML systems can also evolve over time, boosting their ability to counter threats as they emerge. While this is also true of human security teams, using ML removes the possibility of human error from the equation, with people acting as guides and performing roles that the AI cannot undertake.
From Traditional to Modern Security Measures
While early cybersecurity relied on signature-based threat detection, such systems demanded constant updates to ensure they could detect when a threat emerges. There was only a limited possibility of the systems detecting something that fell outside of rigid definitions of potential threats.
As we start to adopt machine learning, however, we can analyze a wider variety of behaviors to look for patterns that could indicate a data breach, including:
- Unusual login activity
- Abnormal data transfer
- Lateral network movement
- Specific file access at strange times
- Significant deviation from historical access patterns
- Privilege escalation attempts
- Anomalous network traffic spikes
The ML can then respond to these with either learned methods or by flagging a system security expert to deal with them. In doing so, the ML system can significantly reduce the effectiveness of zero-day exploits, protect a system, and provide 24/7 surveillance of systems and networks. Previously, such a feat would otherwise have taken a team of specialists to perform, providing round-the-clock coverage.
Preparing for CMMC Compliance with AI-Enhanced Security
While the Cybersecurity Maturity Model Certification (CMMC) is a critical requirement for any organization seeking to work with the DoD and secure Controlled Unclassified Information (CUI), many other federal departments are following suit. As such, many businesses seek to achieve this certification to ensure they can continue doing business with many branches of the government.
As such, you may be seeking to follow the CMMC Final Rule, the regulations by which they determine if you are following requirements.
While it is conceivable that one can provide robust protection for their systems without leveraging ML, that becomes less likely as AI systems become more advanced. By using advanced technologies such as ML, businesses can:
- Maintain their eligibility for government contracts
- Simplify the compliance process
- Enjoy more cost-effective cybersecurity solutions
- Ensure their efficiency and security effectiveness long-term
Groups like Hermathena Labs can help further by offering their expertise in AI-driven tools and secure cloud enclaves. Hermathena Labs configures these solutions to meet CMMC standards from the start. In doing so, they enable you to outsource your secure data handling and storage and expect high levels of protection.
Update to the CMMC Final Rule
The DoD published the CMMC Final Rule (Code of Federal Regulations 32) on December 16, 2024. This latest update introduced mandatory compliance for DoD contractors who may handle CUI. As such, organizations must ensure that they align with specific security regulations to safeguard sensitive data, which can broadly be summarized as:
- Restricted access to authorized users or processes
- Multi-factor authentication for sensitive systems
- Cybersecurity awareness and training
- Detailed audits of system activity
- Secure baseline system configurations
- Development and maintenance of an effective incident response plan
- Security of both physical and digital media
- Physical security for all in-person access
- Encryption of data both in transit and at rest
- Proactive efforts to ensure system and information integrity
While there is no explicit mention of using AI or ML to protect the system or follow these steps, such tools can empower system administrators to perform all of the above with a fraction of the difficulty. They can automate compliance with the above controls and report on their effectiveness, reducing the amount of manual control and hands-on work to fulfill them accurately.
Over the next quarter, we will likely see a significant uptake of ML-based security efforts before the phased rollout starts in Q2 and the federal government starts adding CMMC requirements to new or renewed contracts.
Further Enhancing Cyber Threat Detection With Machine Learning
Recently, we have seen many high-profile cases of actors taking advantage of major vulnerabilities across huge numbers of businesses. For example, more than 30,000 organizations, including those on a state and federal level, fell victim to a breach known as SUNBURST earlier this decade. As such issues occurred before the sudden expansion of machine learning, we need to be aware of two things:
- Systems would have had much more robust security had they been able to detect potential problems earlier
- Cybersecurity threats are likely to grow exponentially more dangerous even than the SUNBURST breach
Cyber threats have the potential to evolve rapidly, using ML to detect patterns in how a system attempts to mitigate their impact.So, more traditional methods of detecting them become insufficient fast as the AI learns how to get around them.
Instead, we should rely on informed security, utilizing effective ML detection systems to analyze the complex data that enters and leaves a system. Ensuring an ML learns effectively from this data will significantly reduce the risk of breaches.
Learning Both Internally and Externally
At the same time, systems must also be aware of internal threats. This is even more important as due to the positioning of the malicious actor, insider threats are among the most challenging to detect.
As such, machine learning systems should try to identify unusual employee behavior to determine if patterns suggest an individual is acting outside of expectations.
ML can also do this over a longer period during an employee's employment. They can cross-reference this with tone and language in e-mails or other habits and determine if someone is more likely to present a threat later on.
Then, they can advise HR of mitigating steps to prevent harm from coming to the company based on what has produced a positive result in the past to ensure there is no reason for concern. Or, if the system detects the employee attempting to take action, it can dynamically restrict the user from accessing specific files and request that a human review the system logs.
Leveraging ML Applications for Cloud Security
The interconnected nature of cloud computing means that systems in such a configuration may be more vulnerable to breaches when not appropriately protected. On the other hand, a well-implemented ML cloud security system can often leverage much more security data due to the frequency at which people access and edit files in a cloud system.
AI-powered tools can automate both the identification and mitigation of potential risks, focusing on cloud-based attacks to offer more robust protection against malicious actors. For example:
New vulnerabilities: Adaptive AI can help integrate on-premises and cloud systems by detecting shared vulnerabilities in the connection.
Compliance checks: Machine learning models can check for compliance with SOC 2, NIST SP 800-171, or the aforementioned CMMC in the cloud.
DDoS prevention: The system can more readily detect potential DDoS attacks based on historical data and recommend areas of improvement.
Historical access analysis: A legacy of data about user access history can prevent shared files from becoming compromised.
By combining these with multi-factor authentication brokers and quantum-enhanced encryption, such a system can offer vastly more comprehensive levels of security.
It can recommend traffic throttling or IP blacklisting in the case of a DDoS. Alternatively, it could scan for sensitive data that might be leaving the cloud and prevent it until someone takes specific steps to ensure its security.
Secure Your Future with ML Cybersecurity
Machine learning in cybersecurity is both here to stay and likely to make a significant impact in the next decade. It provides businesses with stronger defenses, proactive threat detection, and clear steps to safeguard your data.
Hermethena Labs specializes in transforming businesses' cybersecurity efforts by providing AI-powered CMMC-compliant data solutions. Contact us today to leverage our decades of experience designing and modernizing existing systems to ensure you comply with your future contractual requirements.