AI is transforming enterprise security, enabling faster detection, automated response, and scalable monitoring across hybrid environments. But autonomous systems can also misclassify, misconfigure, or even cause outages themselves. How can your organization balance AI’s benefits with oversight, rollback, and recovery strategies to ensure resilience against both attackers and AI mistakes?
What is AI in Cybersecurity?
Artificial intelligence is rapidly reshaping cybersecurity. Enterprises are leaning on AI to keep pace with an evolving threat landscape. Machine learning models can spot anomalies in log data. Generative AI tools can draft incident playbooks. Indeed, the speed and scale of AI makes it possible to identify attacks faster than human analysts alone ever could.
But AI introduces new challenges of its own. Autonomous systems can misclassify benign activity, apply flawed policies at scale, or even trigger incidents themselves.
As organizations deploy more AI to defend their environments, they also need strategies for oversight, monitoring, and recovery when these powerful tools go astray. The following sections explore both sides of this transformation: the breakthroughs in detection and the urgent need for resilience when AI itself becomes the problem.
How Does AI Detect Threats vs. How Do We Fix AI Mistakes
One of the things AI does best is ingest massive, multi-source datasets and use them to build behavioral baselines for users and systems. In cybersecurity, those datasets include things like log streams, backup snapshots, user file behavior, configuration changes, etc.. AI uses pattern recognition and User and Entity Behavior Analytics (UEBA) to flag things that diverge from the norm. This process can expose vulnerabilities such as zero-day exploits, advanced persistant threats, or insider threats.
There are several different machine learning approaches that have traditionally powered cybersecurity:
Supervised learning: Tools are trained on labeled malicious vs. benign activity. Good for recognizing known threats.
Unsupervised learning: Tools detect statistical anomalies not seen in training sets. Ideal for zero-day exploits or stealth insider activity.
Reinforcement learning: Tools adapt to changing environments, learning optimal responses through trial and reward cycles.
These techniques have existed and helped power infosec platforms for years, providing real-world benefits. For example, AI powers early ransomware detection via analysis of anomalous behavior patterns. It also aids phishing identification using natural language processing (NLP) analysis of suspicious emails and malware classification through deep learning that more accurately distinguishes benign from harmful binaries. These techniques work together to produce higher detection rates, fewer false positives, and faster containment.
The recent revolution in generative AI has introduced new capabilities. Generative AI tools can automate policy creation, generate incident response playbooks, and enable analysts to query threats more fluidly. By modeling user behavior over time, GenAI builds evolving profiles that make anomalous spikes or compromised accounts easier to spot.
However, generative AI can also misstep. A poorly tuned playbook generator could push flawed firewall rules. A misguided account model could lock out legitimate users. AI agents hallucinate and make mistakes all the time. Imagine an AI agent misclassifying routine changes as anomalous—or worse, propagating a flawed change across systems. The fallout can be severe if corrective controls aren’t in place.
That’s why post-incident recovery is so important. As AI automation becomes more widespread, security teams need forensic analysis to trace AI missteps, audited log trails to pinpoint what went wrong, and selective rollback of changes to restore to a previous state without undermining integrity.
Benefits of AI for Enterprise Cybersecurity
The enterprise benefits of AI are undeniable:
Faster and more accurate threat detection with reduced false positives
Round-the-clock automated security
Scalable log analysis across cloud and on-prem systems
Cost savings by automating repetitive analyst tasks
Coupling AI with oversight, audit trails, and recovery establishes resilience—even when AI systems themselves introduce errors. It's increasingly important that organizations balance autonomy and governance as they scale up their use of AI.
Automation and Recovery Time Reduction (MTTR)
Security operation center (SOC) teams lead the response to breaches and developing attacks using tested cybersecurity techniques. For example, they isolate compromised endpoints, block access to sensitive data from accounts that are behaving suspiciously, and update firewalls to keep pace with zero days and the latest threat intelligence. By automating processes like these, enterprise SOC teams can reach the longtime dream of shortening mean time to recovery (MTTR) from hours to minutes.
But organizations are still developing strategies for recovery from AI-caused mistakes—call it AI-MTTR.
AI Operational Risks
AI can integrate with SIEM, SOAR, and XDR tools to continuously monitor across environments. But one blind spot is real-time monitoring of the AI agents themselves. Without oversight, a misfiring AI could disable hundreds of accounts or misclassify terabytes of data. Early-warning systems for autonomous AI decision errors are just as important as predictive analytics for external threats.
AI agents can amplify risk when a logic error or bad data input cascades through production systems. These aren’t hypothetical scenarios; a single flawed prompt could disrupt workflows at scale. Consider the July 2025 incident when an AI agent deleted a company's entire codebase.
When AI agents introduce configuration drift, misapply policies, or cause workflow collapses, that's a whole different type of problem than an external attack. These errors stem from inside the enterprise rather than from outside adversaries—and can propagate rapidly, thanks to AI autonomy—compounding the damage.
Distinguishing AI accidents from malicious cyberattacks requires fine-grained forensic data. Without that information, security teams may end up conflating two very different problems. Enterprises need to know whether they are under attack or recovering from their own misguided AI.
Post-Incident AI Damage Recovery
Recovery in the AI era requires more than restoring from backups. Enterprises need real-time auditing of AI decisions, granular rollback that can reverse only harmful changes, robust impact assessments to calculate downstream damage, and above all, fast recovery to minimize costly downtime. These capabilities will become the foundation of resilience as companies embed AI deeper into daily operations. The organizations that succeed will be those that treat AI recovery with the same seriousness as AI-driven detection.
When an organization’s AI agent itself creates the incident—deleting data, reconfiguring permissions, or propagating flawed changes—traditional MTTR metrics and strategies don't apply. Conventional backup and restore processes often lack the precision to roll back AI-driven actions. Enterprises need recovery methods purpose-built for AI errors, with forensic clarity to understand cause and scope and rollback mechanisms to undo unintended changes before cascading disruptions take hold.
Rubrik has addressed this gap with Agent Rewind, which records AI agent actions, links them to their originating prompts, and enables selective rollback. That means enterprises can not only restore lost data but also undo specific AI-driven changes—preserving resilience and containing unintended consequences.
And recovery itself is becoming more intelligent. Ruby, the generative AI companion of Rubrik Security Cloud, accelerates cyber detection, recovery, and resilience for all levels of cyber expertise. As soon as a threat is detected by Rubrik Anomaly Detection, Ruby presents interactive guidance and recommendations to swiftly isolate and recover the infected data. By combining forensic audit trails, selective rollback, and AI-assisted guidance, enterprises can be sure that when AI makes mistakes—as it inevitably will—recovery is as fast, targeted, and reliable as the threats are unpredictable.