As cyberattacks become more frequent and complex, businesses are struggling to keep up. Highly trained security teams work around the clock to spot and stop digital intruders, but it often feels like a losing battle. Pirates always seem to have the advantage.
However, there is a light at the end of the tunnel. A new wave of artificial intelligence technologies could tip the scales in favor of defenders. By using self-learning programs as digital allies, security analysts can strengthen their efforts to protect corporate networks and devices, without spending a ton of additional resources.
One area of cybersecurity where AI is having a big impact is endpoint detection and response (EDR). This essentially acts as an early warning system for attacks, closely monitoring computers, phones, and other endpoints to detect subtle characteristics of a cyber-attack in progress. Whenever something seems abnormal, EDR sounds the alarm so human experts can investigate. It can even take basic steps, like isolating compromised devices, to save time.
But will AI-powered EDR completely replace and eliminate the need for human intervention? The simple answer is no. As we see in many AI applications, the best results seem to come when AI and humans work together, not in place of each other. Let’s see why this is the case.
The Promise of AI-Powered EDR
BDU The tools have become essential weapons for identifying, analyzing, and remediating ever-evolving attacks on massive numbers of devices. Today, many of the leading EDR platforms leverage artificial intelligence to augment human capabilities, improving accuracy and efficiency.
Using supervised machine learning algorithms trained on mountains of threat data, AI-driven EDR can:
- Spot new attack patterns and behaviors. By analyzing system events and comparing large data sets, AI detects anomalies that human analysts would likely miss. This allows your team to identify and stop stealth attacks that other tools can’t see.
- Provide context with an automated survey. AI can instantly trace the full extent of an incident, looking for signs of compromise in your environment. This reduces the tedious work of analysts to understand the root causes.
- Prioritize the most critical incidents. Not all alerts require the same level of urgency, but it can be difficult to distinguish between an insignificant alert and a serious one. AI assessments highlight the most dangerous threats to attract valuable human attention.
- Recommend optimal responses tailored to each attack. Based on the specifics of malware strains, exploited vulnerabilities, etc., AI suggests the best containment and remediation actions to eliminate the threat with surgical precision.
Increasing AI allows analysts to work smarter and faster by handling much of the heavy lifting of threat detection, investigation, and recommendations. However, human expertise and critical thinking remain essential to connect the dots.
The human element: judgment, creativity, intuition
Although AI is excellent at analyzing data, human analysts bring key strengths to endpoint defense that machines lack. People offer three crucial capabilities:
Balanced assessment
AI can sometimes flag harmless events as suspicious, causing false alarms, or miss real threats. But human experts can use their experience and good judgment to evaluate AI findings. For example, if the system incorrectly labels a normal software update as malicious, an analyst can check it and correct the error, avoiding unnecessary downtime. This balanced human assessment allows for more accurate threat detection.
Creative problem solving
Attackers continue to modify their malware to defeat AI systems, which are often configured to detect known threats. But human analysts can think outside the box and identify new or subtle threats based on small quirks. When hackers change tactics, analysts can come up with creative new detection rules based on tiny anomalies in the code – information that machines would have difficulty picking up on.
See the big picture
Protecting complex networks requires taking into account many changing factors that algorithms cannot fully account for. In the midst of a sophisticated attack, human judgment becomes essential in making high-stakes decisions, such as whether to isolate systems or negotiate a ransom. Even though AI can suggest options, the human perspective is still necessary to guide the response and minimize the impact on the business.
Together, human insight and AI provide a powerful defense capable of detecting advanced cyberattacks that other systems might miss. AI processes data quickly, while human reasoning fills in the gaps. By working together, people and AI strengthen endpoint protection.
Optimize the Human-AI Security Team
Here are some tips to help you get the most out of your AI-enhanced EDR with human-led teams:
- Trust but verify AI ratings. Leverage AI detections to quickly assess incidents, but validate results with manual research before taking action. Don’t blindly trust every alert.
- Use AI to focus on human expertise. Let AI handle repetitive tasks like endpoint monitoring and gathering threat details so analysts can focus their energy on higher-value efforts like strategic response planning and proactive hunting.
- Provide feedback to improve AI models over time. Adding human validation into the system – confirming true/false positives – allows the algorithms to self-correct to become more accurate. AI learns from human wisdom over time.
- Collaborate with AI every day. The more analysts and AI work together, the more both parties learn, improving their skills and performance. Daily use enriches knowledge.
Just as cyber adversaries leverage automation and AI for their attacks, defenders must fight back with an AI-powered arsenal. Endpoint security, based on artificial and human intelligence, is the best hope for securing our digital world.
When man and machine join forces, harnessing their complementary capabilities to outwit and outwit any adversary, there is no limit to what we can achieve together. The future of cybersecurity is here – and it’s a human-AI partnership.
Challenges to Adoption of AI-augmented EDR
Implementing AI for security monitoring sounds great in theory. But for already overworked teams, making this work can be complicated in practice. People face all kinds of obstacles when deploying this advanced technology, from understanding how the tools work to stopping
exhaustion of the alarm.
The complexity
Security analysts who use EDR tools on a daily basis are not always professional engineers. So, expect them to intuitively grasp confidence intervals, accuracy rates, model optimization, and other machine learning ideas? It’s a big challenge. Without plain language training to demystify concepts, the bells and whistles of AI are never used to catch bad actors.
Drowning in false positives
Particularly early on, some AI tools have moved beyond the limits of threat tagging. Suddenly, analysts began drowning in hundreds of low-confidence alerts every week – many of which were false. This buried critical signals in the noise. Feeling overwhelmed, many teams might end up ignoring alerts altogether. Tools need to be optimized and refined so that there is a balance in sensitivity.
Black box tools
Neural networks work like impenetrables black boxes. Because the rationale for risk scores and recommendations remains opaque, staff struggle to trust an automated system to take the lead. For AI to gain credibility with its human colleagues, it must allow them to peek under the hood to understand its reasoning – but this is not always possible with current technology.
More than a miracle solution
Introducing new AI tools alone will not be enough. To fully utilize technology, security teams must improve their processes, skills, policies, metrics, and even cultural norms to realign with it. Deploying AI as a turnkey package without actually scaling the organization will permanently lock out all of this game-changing potential.
Last word
AI brings a wide range of exciting tools and defenses against cybersecurity threats. While this is good news, much of it will remain potential until AI and human teams can work together in harmony, leveraging each other’s strengths. EDR is an area of cybersecurity that relies particularly on a harmonious partnership between machine intelligence and human expertise.
Of course, there is a learning curve that goes both ways. AI systems need to do a better job of conveying their internal logic to human teammates in transparent terms that they can respond to intuitively. Solving the signal-to-noise problem in early warning systems will also help prevent analyst fatigue and disconnection.
