Beat AI to the Gap: Closing Defensive Weaknesses Before AI Attacks Exploit Them

AI Has Accelerated the Speed, Scale, and Volume of Attacks

The speed of AI-powered attacks is mind-numbing. CrowdStrike found that average eCrime breakout time fell to 29 minutes, with the fastest recorded breakout at 27 seconds. Armadin showed an LLM-driven NTLM relay attack completing in under three minutes, then roughly 1.5 minutes with BloodHound MCP context.

It’s not just the speed, but the scale. Anthropic reported an AI-orchestrated cyber-espionage campaign where AI performed 80–90% of the operation, required human input at only 4–6 critical decision points, and made thousands of requests at peak activity, often multiple per second. The recent Mythos evaluation points in the same direction: multi-stage attacks executed in minutes what would usually take hours or days for a group of humans to execute.

And the volume is crushing. CrowdStrike reported an 89% year-over-year increase in attacks by AI-enabled adversaries.

This is the pressure curve defenders are operating against now. AI gives attackers a way to search faster, test faster, execute faster, adapt faster, and scale their attacks – and do it all with less human labor required to keep the attacks moving.

Misconfigured Security Controls are Defensive Weaknesses. AI-Powered Attacks Find Them Fast.

This acceleration has created an urgency for security teams looking to harden their defenses.

Misconfigured security controls, unused security capabilities, and configuration drift create defensive weaknesses. And perhaps in a slower attacker environment, they were often treated like security hygiene issues: important to fix, but easy to push behind incidents, alerts, audits, and other seemingly higher-priority projects.

AI changes that calculation. If attackers can recon faster, test more paths, automate more steps, and chain actions together in minutes, then defensive weaknesses become much easier to find and exploit. A stale firewall rule, a weakened endpoint policy, an inactive email security control, a conditional access exception, or a SASE control that drifted away from baseline security policy can become the opening an AI-powered attack finds before the security team knows about it.

Reach’s drift research shows how common these weaknesses are. The average organization uses 35 distinct cybersecurity products, which amounts to thousands of security controls to manage. Security products receive updates throughout the year, and across a 35-product stack, that amounts to roughly 700 new or updated features released annually for security teams to understand, configure, validate, and maintain. It’s just too much to juggle, even for a well-staffed team. Across thousands of controls, some will inevitably drift out of alignment unbeknownst to the security team. Every vendor update, business change, access setting tweak, and temporary exception creates another chance for defenses to drift, creating an opening for attackers.

The result: 97% of security professionals have experienced either a confirmed breach or near miss due to a cybersecurity tool misconfiguration in the past year. In the AI era, that percentage will only increase. All of those defensive weaknesses will become part of the attacker’s search space.

Defenders Need to Close Gaps Before AI-Powered Attacks Exploit Them

This is why finding and fixing defensive weaknesses has become so urgent.

Security teams need to identify misconfigurations, unused capabilities, and configuration drift faster than ever. They need to understand which gaps create real exposure, which controls are not operating as intended, which fixes should happen first, and then remediate as soon as possible to close gaps before AI-powered attacks can uncover and exploit them.

Unfortunately, human-powered defense alone cannot keep up with that requirement.

That’s not a criticism of security teams. It’s just math. Even well-staffed teams with decades of experience cannot manually validate every control, review every configuration change, understand every vendor-specific setting, prioritize every exposure, and remediate every issue across identity, endpoint, email, firewall, SASE, and cloud environments – and do it at the breakneck speed that AI-powered offense can operate.

The defensive side is too slow in comparison. Reach’s drift research found that organizations review cybersecurity tool configurations an average of 6.5 times per month, but once a misconfiguration or drift issue is identified, remediation takes an average of 8.3 days. Only 2% of respondents said they can fix issues in less than a day. It’s simply not fast enough to keep up with AI-fueled attacker-side speed and precision.

Attackers are compressing the time between weakness and exploitation. Defenders need to compress the time between finding and fixing.

Traditional automation can help, but scripts, scanners, checklists, and point-in-time reviews cannot solve the full problem. Defenders need speed, scale, and contextual understanding of their security controls. They need continuous visibility into exploitable openings and a way to harden defenses before weaknesses become incidents.

Only an AI-native defense can outpace what AI-powered attackers now have.

Reach Hardens Security Controls at the Speed of AI

Reach helps security teams stop AI-powered attacks using AI-powered defenses.

Reach is the AI-native operating system for your security controls. It connects with your existing security technology stack to continuously analyze the security control plane across your defenses. At the core of Reach is MastermindAI – a purpose-built AI engine powered by a mixture of precise reasoning models and cybersecurity domain-specific language models (DSLMs). These models are trained on real-world security data, threat context, and an intimate understanding of your specific environment and your security controls.

Using these DSLMs that uniquely understand your specific environment and what your tools are capable of (and configuration detail), MastermindAI analyzes millions of data points across your environment to decipher how effectively your defensive controls are being used – and misused – across your people, assets, and systems. Reach marries this precise analysis with real-world security data and up-to-date threat context. This unique comprehension of both attacker techniques and your defensive controls allows Reach to rapidly uncover and proactively pinpoint exposures. Reach provides context around what changed, why it matters, and how controls are exposed. It then prioritizes action based on exposure, control context, attacker behavior, and business risk, so teams know what to fix first.

Reach doesn’t just find defensive weaknesses. It provides an operational framework to automatically remediate them. Security teams can use Reach to quickly fix misconfigurations, activate unused capabilities, and bring controls back into alignment with security baselines. Over time, Reach continuously validates that those controls remain properly configured and alerts teams when drift occurs.

This strengthens posture while reducing the manual work that slows teams down: investigating configuration issues, determining whether a change created meaningful risk, piecing together vendor-specific settings, deploying newly released features, and chasing remediation through ticketing workflows.

Instead of relying on periodic reviews and manual validation, teams get proactive control hardening across the security stack they already own.

The results are actually measurable. Using Reach, Insurity saved 95 hours per employee per month. Nutanix achieved 95% faster detection of security control issues. Aristocrat reduced its control maintenance workload by 89%.

That is what AI-powered defense should deliver: proactive security hardening, faster detection, less manual work, tighter controls, and more time back to the teams responsible for keeping the business secure.

AI-powered attacks are moving faster, scaling wider, and increasing in volume. Defenders need AI-powered defenses to quickly deny AI-attackers an opening, and close security control gaps before AI attacks can exploit them.