#ai attacks

Ai and the future of work “the atlantic” march 26

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Adversarial AI Attacks? Yes, your AI-powered security tools have a fatal flaw: they can be tricked. Attackers are now using adversarial machine learning to slip past your defences undetected, and most security teams don’t even know it’s happening. Adversarial AI Attacks: What You Need to Know Adversarial AI attacks exploit fundamental weaknesses in machine learning, letting attackers bypass…

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Security Teams Are Fixing the Wrong Threats. Here’s How to Course-Correct in the Age of AI Attacks

Cyberattacks are no longer manual, linear operations. With AI now embedded into offensive strategies, attackers are developing polymorphic malware, automating reconnaissance, and bypassing defenses faster than many security teams can respond. This is not a future scenario, it’s happening now.

At the same time, most security defenses are still reactive. They rely on identifying known indicators of compromise, applying historical attack patterns, and flagging risks based on severity scores that may not reflect the true threat landscape. Teams are overwhelmed by volume, not insight, creating a perfect environment for attackers to succeed.

The industry’s legacy mindset built around compliance checklists, periodic assessments, and fragmented tooling has become a liability. Security teams are working harder than ever, yet often fixing the wrong things.

Why This Gap Exists

The cybersecurity industry has long leaned on risk scores like CVSS to prioritize vulnerabilities. However, CVSS scores don’t reflect the real-world context of an organization’s infrastructure such as whether a vulnerability is exposed, reachable, or exploitable within a known attack path.

As a result, security teams often spend valuable time patching non-exploitable issues, while attackers find creative ways to chain together overlooked weaknesses and bypass controls.

The situation is further complicated by the fragmented nature of the security stack. SIEMs, endpoint detection and response (EDR) systems, vulnerability management (VM) tools, and cloud security posture management (CSPM) platforms all operate independently. This siloed telemetry creates blind spots that AI-enabled attackers are increasingly adept at exploiting.

Signature-Based Detection Is Fading

One of the most concerning trends in modern cybersecurity is the diminishing value of traditional detection methods. Static signatures and rule-based alerting were effective when threats followed predictable patterns. But AI-generated attacks don’t play by those rules. They mutate code, evade detection, and adapt to controls.

Take polymorphic malware, which changes its structure with each deployment. Or AI-generated phishing emails that mimic executive communication styles with alarming accuracy. These threats can slip past signature-based tools entirely.

If security teams continue to rely on identifying what has already been seen, they’ll remain one step behind adversaries who are continuously innovating.

Regulatory Pressure Is Mounting

The problem isn’t just technical, it’s now regulatory. The U.S. Securities and Exchange Commission (SEC) recently introduced new cybersecurity disclosure rules, requiring public companies to report material cybersecurity incidents and describe their risk management strategies in real time. Similarly, the European Union’s Digital Operational Resilience Act (DORA) demands a shift from periodic assessments to continuous, validated cyber risk management.

Most organizations are not prepared for this shift. They lack the ability to provide real-time assessments of whether their current security controls are effective against today’s threats, especially as AI continues to evolve those threats at machine speed.

Threat Prioritization Is Broken

The core challenge lies in how organizations prioritize work. Most still lean on static risk scoring systems to determine what gets fixed and when. These systems rarely account for the environment in which a vulnerability exists, nor whether it’s exposed, reachable, or exploitable.

This has led to security teams spending significant time and resources fixing vulnerabilities that aren’t attackable, while attackers find ways to chain together lower-scoring, overlooked issues to gain access. The traditional “find and fix” model has become an inefficient and often ineffective way to manage cyber risk.

Security must evolve from reacting to alerts toward understanding adversary behavior—how an attacker would actually move through a system, which controls they could bypass, and where the true weaknesses lie.

A Better Way Forward: Proactive, Attack-Path-Driven Defense

What if, instead of reacting to alerts, security teams could continuously simulate how real attackers would try to breach their environment, and fix only what matters most?

This approach, often called continuous security validation or attack-path simulation, is gaining momentum as a strategic shift. Rather than treating vulnerabilities in isolation, it maps how attackers could chain misconfigurations, identity weaknesses, and vulnerable assets to reach critical systems.

By simulating adversary behavior and validating controls in real time, teams can focus on exploitable risks that actually expose the business, not just the ones flagged by compliance tools.

Recommendations for CISOs and Security Leaders

Here’s what security teams should prioritize today to stay ahead of AI-generated attacks:

Implement Continuous Attack Simulations Adopt automated, AI-driven adversary emulation tools that test your controls the way real attackers would. These simulations should be ongoing not just reserved for annual red team exercises.

Prioritize Exploitability Over Severity Move beyond CVSS scores. Incorporate attack path analysis and contextual validation into your risk models. Ask: Is this vulnerability reachable? Can it be exploited today?

Unify Your Security Telemetry Consolidate data from SIEM, CSPM, EDR, and VM platforms into a centralized, correlated view. This enables attack-path analysis and improves your ability to detect complex, multi-step intrusions.

Automate Defense Validation Shift from manual detection engineering to AI-powered validation. Use machine learning to ensure your detection and response strategies evolve alongside the threats they’re meant to stop.

Modernize Cyber Risk Reporting Replace static risk dashboards with real-time exposure assessments. Align with frameworks like MITRE ATT&CK to demonstrate how your controls map to real-world threat behaviors.

Organizations that shift to continuous validation and exploitability-based prioritization can expect measurable improvements across multiple dimensions of security operations. By focusing only on actionable, high-impact threats, security teams can reduce alert fatigue and eliminate distractions caused by false positives or non-exploitable vulnerabilities. This streamlined focus enables faster, more effective responses to real attacks, significantly reducing dwell time and improving incident containment.

Moreover, this approach enhances regulatory alignment. Continuous validation satisfies growing demands from frameworks like the SEC’s cybersecurity disclosure rules and the EU’s DORA regulation, both of which require real-time visibility into cyber risk. Perhaps most importantly, this strategy ensures more efficient resource allocation and allows teams to invest their time and attention where it matters most, rather than spreading themselves thin across a vast surface of theoretical risk.

The Time to Adapt Is Now

The era of AI-driven cybercrime is no longer a prediction, it’s the present. Attackers are using AI to find new paths in. Security teams must use AI to close them.