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Introduction:
The National Academies of Sciences, Engineering, and Medicine recently released a rapid expert consultation on the implications of AI for cybersecurity, delivering a sobering verdict: AI is reshaping the cyber battlefield faster than society can measure or respond. Frontier AI systems are rapidly expanding what is possible for both attackers and defenders, but in the near term, these advances are likely to favor attackers by reducing the time, expertise, and operational effort required for cyberattacks. The report concludes that “AI may widen the near-term gap between attackers and defenders” — a polite academic way of saying that offensive AI capabilities are already outpacing defensive readiness.
Learning Objectives:
- Understand how generative AI and agentic systems are shifting the offense-defense balance in cybersecurity
- Identify specific AI-powered attack vectors and defensive countermeasures applicable to enterprise environments
- Master practical commands and configurations for AI-enhanced threat detection, vulnerability assessment, and incident response
You Should Know:
- The Offensive AI Arsenal: From Phishing to Automated Exploitation
The attacker’s playbook has been rewritten. AI is no longer a theoretical threat — it is an operational weapon. Threat actors are leveraging large language models to generate highly convincing phishing emails, messages, and fake websites that expand beyond attackers’ native languages. According to Mimecast’s 2025 Global Threat Intelligence Report, phishing now represents 77% of all observed attacks, up from 60% in 2024, a surge driven by the growing use of generative AI tools among threat groups. Social engineering and business email compromise attacks increased from 20% to 25.6% in early 2025 compared to the same period in 2024, likely due to AI’s role in crafting convincing impersonations.
Beyond social engineering, AI is automating the technical side of hacking. Researchers have demonstrated that LLMs can generate functional exploit code for vulnerabilities like buffer overflows. Tools like PentestGPT and AutoPentester can automate penetration testing workflows, with AI agents conducting iterative reconnaissance and exploitation against target IPs. The HexStrike AI framework reportedly reduces exploitation time from several days to under 10 minutes, orchestrating more than 150 security utilities through AI agents.
Defensive Countermeasure — AI-Powered Phishing Detection with SPF, DKIM, and DMARC:
To combat AI-enhanced phishing, organizations must enforce email authentication at the DNS level. Below are the essential commands to configure and verify these protections on Linux:
Check current SPF, DKIM, and DMARC records for a domain dig +short TXT example.com | grep -E "spf|dkim|dmarc" Add SPF record (replace with your actual mail servers) In your DNS zone file, add: example.com. IN TXT "v=spf1 ip4:192.0.2.0/24 include:_spf.google.com ~all" Generate DKIM keys (using OpenDKIM) sudo opendkim-genkey -D /etc/opendkim/keys/ -d example.com -s default sudo chown opendkim:opendkim /etc/opendkim/keys/default.private Add DKIM record (the output from the above command provides the TXT record) Add DMARC record In your DNS zone file, add: _dmarc.example.com. IN TXT "v=DMARC1; p=quarantine; rua=mailto:[email protected]"
On Windows (using PowerShell for DNS verification):
Resolve-DnsName -Type TXT example.com | Where-Object {$_.Strings -match "spf|dkim|dmarc"}
Resolve-DnsName -Type TXT _dmarc.example.com
- The Measurement Crisis: Why We Can’t Tell How Bad It Really Is
The National Academies report highlights a critical gap: AI-enabled cyber capabilities are evolving faster than the ability to evaluate and measure them. Current benchmarks are sporadic and fail to provide a systematic view of security outcomes. This is not merely an academic concern — without reliable metrics, organizations cannot quantify their AI-related risk exposure or make informed investment decisions.
Recent efforts are beginning to address this gap. The Cybersecurity AI Benchmark (CAIBench) integrates five evaluation categories covering over 10,000 instances, including Jeopardy-style CTFs, Attack and Defense CTFs, and cyber range exercises. Evaluation of state-of-the-art AI models reveals saturation on security knowledge metrics at roughly 70% success, but substantial degradation in multi-step adversarial scenarios at only 20–40% success. This demonstrates a pronounced gap between conceptual knowledge and adaptive capability. Similarly, CyberSOCEval — a benchmark suite from Meta and CrowdStrike — evaluates LLMs on malware analysis and threat intelligence reasoning, revealing that current models are far from saturating these evaluations.
Practical Step — Implementing Continuous Security Benchmarking:
Organizations should establish a continuous benchmarking program to measure their AI security posture. Below is a Linux-based script to automate security control validation:
!/bin/bash security_benchmark.sh - Continuous Security Posture Assessment <ol> <li>Vulnerability scan with OpenVAS or Nmap nmap -sV --script vuln target_ip -oA scan_results</p></li> <li><p>Check for open SMB ports (common attack vector) nmap -p 445 --open target_ip</p></li> <li><p>Verify firewall rules sudo iptables -L -1 -v | grep -E "ACCEPT|DROP|REJECT"</p></li> <li><p>Check for outdated packages with known CVEs sudo apt-get update && sudo apt-get upgrade --dry-run | grep -i security</p></li> <li><p>Audit SUID binaries (potential privilege escalation vectors) find / -perm -4000 -type f 2>/dev/null | xargs ls -la</p></li> <li><p>Check SSH configuration for weak settings grep -E "PermitRootLogin|PasswordAuthentication|Protocol" /etc/ssh/sshd_config
On Windows (PowerShell equivalent):
Check for open ports
Test-1etConnection -ComputerName target_ip -Port 445
Get firewall rules
Get-1etFirewallRule | Where-Object {$_.Enabled -eq "True"}
Check installed security updates
Get-HotFix | Select-Object -First 10
Audit services running with SYSTEM privileges
Get-Service | Where-Object {$<em>.Status -eq "Running" -and $</em>.StartType -eq "Automatic"}
- The Non-Tech Company Problem: At the Mercy of Vendors
Perhaps the most alarming finding from the report concerns non-technical organizations. As Ilya Kabanov noted, the risks for non-tech companies are elevating faster and higher because they have fewer levers for risk management. These organizations are at the mercy of software vendors, hoping that vulnerabilities are found and patched quickly enough. When AI accelerates the vulnerability discovery and exploitation cycle, this dependency becomes existential.
The challenge is compounded by the proliferation of open-weight AI models. While these models are crucial for innovation and transparency, they also pose unique risks: harmful AI capabilities can proliferate rapidly and irreversibly. Open-weight model safeguards can be removed through fine-tuning, creating a “safety gap” between the officially released version and a more dangerous version accessible through attack methods. Red teaming of open-weight models reveals profound susceptibility to adversarial manipulation, with multi-turn attack success rates observed to be 2x to 10x higher than single-turn attacks.
Vendor Risk Management Framework — Practical Implementation:
For non-technical organizations, vendor risk management must become a priority. Below is a structured approach using open-source tools:
Linux: Using OWASP Dependency-Check to scan vendor software components Install Dependency-Check wget https://github.com/jeremylong/DependencyCheck/releases/download/v9.0.0/dependency-check-9.0.0-release.zip unzip dependency-check-9.0.0-release.zip ./dependency-check/bin/dependency-check.sh --scan /path/to/vendor/software --format HTML --out report.html Using Trivy for container vulnerability scanning (vendor-supplied containers) trivy image vendor/application:latest --severity HIGH,CRITICAL --format table Using OSS-Fuzz for fuzzing vendor libraries (if source available) Install OSS-Fuzz dependencies sudo apt-get install -y python3-pip git git clone https://github.com/google/oss-fuzz.git cd oss-fuzz python3 infra/helper.py build_fuzzers --sanitizer=address vendor_project python3 infra/helper.py run_fuzzer vendor_project fuzz_target
Windows (using PowerShell for vendor software inventory):
Inventory all installed software (for vendor exposure assessment) Get-WmiObject -Class Win32_Product | Select-Object Name, Version, Vendor | Export-Csv vendor_inventory.csv Check for known vulnerable software versions using public CVE databases (requires internet access and API key from NVD) Invoke-RestMethod -Uri "https://services.nvd.nist.gov/rest/json/cves/2.0?keywordSearch=software_name" | ConvertTo-Json
4. The Long Game: AI-Enabled Defense-in-Depth
Despite the grim near-term outlook, the National Academies report offers cautious optimism for the longer term. AI may enable a fundamentally stronger defensive posture, allowing for a transition from static, episodic defense to continuous ‘defense-in-depth’ — where vulnerability discovery and patching, threat detection, intelligence generation, and incident response operate as ongoing, interconnected processes. Security teams that are often stretched thin may be able to leverage AI-enabled tools to improve threat detection, identify and remediate vulnerabilities, support incident response, and facilitate threat intelligence sharing across organizations.
AI-enhanced Defense-in-Depth (AI-E-DiD) leverages machine learning to analyze network traffic and detect intrusions in real-time. Secure AI by Design principles, drawing from CISA’s framework, embed security controls at every phase of the Machine Learning Security Operations (MLSecOps) lifecycle. The goal is to shorten the interval between the current high-risk regime and a future where adaptive, scalable, and resilient defensive capabilities are the norm.
Building an AI-Enhanced Detection Pipeline:
Below is a practical implementation of an AI-enhanced threat detection pipeline using open-source tools on Linux:
!/bin/bash ai_threat_detection_pipeline.sh - Continuous AI-Enhanced Security Monitoring <ol> <li>Deploy Suricata for network traffic analysis with ML-enhanced rules sudo apt-get install suricata sudo suricata-update sudo systemctl start suricata</p></li> <li><p>Set up Zeek (formerly Bro) for network metadata extraction sudo apt-get install zeek sudo zeekctl deploy</p></li> <li><p>Configure Wazuh (OSSEC fork) for host-based intrusion detection with AI correlation curl -s https://packages.wazuh.com/key/GPG-KEY-WAZUH | sudo apt-key add - echo "deb https://packages.wazuh.com/4.x/apt/ stable main" | sudo tee /etc/apt/sources.list.d/wazuh.list sudo apt-get update && sudo apt-get install wazuh-agent sudo systemctl start wazuh-agent</p></li> <li><p>Set up Elastic Stack for log aggregation and ML-based anomaly detection Install Elasticsearch, Logstash, Kibana (ELK stack) Configure ML jobs in Kibana for anomaly detection on security events</p></li> <li><p>Deploy Falco for runtime security monitoring (Kubernetes/containers) curl -s https://falco.org/repo/falcosecurity-packages.asc | sudo apt-key add - echo "deb https://download.falco.org/apt/ stable main" | sudo tee /etc/apt/sources.list.d/falcosecurity.list sudo apt-get update && sudo apt-get install falco sudo systemctl start falco
Windows (using Sysmon and PowerShell for advanced detection):
Install Sysmon for detailed event logging Download Sysmon from Microsoft Sysinternals Install with comprehensive configuration .\Sysmon64.exe -accepteula -i sysmon_config.xml Configure Windows Event Forwarding for centralized analysis Enable PowerShell logging for script block detection Set-ItemProperty -Path "HKLM:\SOFTWARE\Policies\Microsoft\Windows\PowerShell\ScriptBlockLogging" -1ame "EnableScriptBlockLogging" -Value 1 Use Windows Defender ATP (or Microsoft Defender for Endpoint) ML-based detection Configure tamper protection and cloud-delivered protection Set-MpPreference -EnableTamperProtection $true Set-MpPreference -CloudBlockLevel High Set-MpPreference -CloudTimeout 50
5. Policy and Investment: The Missing Piece
The National Academies report emphasizes that realizing AI’s defensive promise will require investment across technical, architectural, and institutional dimensions. Measures such as restricted access to the most advanced AI models may help buy time for defenders, but long-term security will depend less on limiting access and more on building resilient systems. As co-author Nadya T. Bliss noted, “Without incentives that are aligned to security outcomes, security will continue to receive less attention than capability deployment”.
Automated Compliance and Policy Enforcement:
Organizations should implement automated compliance enforcement to bridge the gap between policy and practice:
!/bin/bash compliance_audit.sh - Automated Security Policy Enforcement <ol> <li>CIS Benchmark compliance check (using OpenSCAP) sudo apt-get install openscap-scanner oscap xccdf eval --profile xccdf_org.ssgproject.content_profile_cis --results compliance_results.xml /usr/share/xml/scap/ssg/content/ssg-ubuntu-2204-ds.xml</p></li> <li><p>Check for insecure service configurations Verify that unnecessary services are disabled systemctl list-unit-files | grep enabled | grep -E "telnet|ftp|rlogin|rsh"</p></li> <li><p>Verify that SELinux/AppArmor is enforcing sudo aa-status || sudo sestatus</p></li> <li><p>Audit password policies sudo grep -E "^PASS_MAX_DAYS|^PASS_MIN_DAYS|^PASS_WARN_AGE" /etc/login.defs</p></li> <li><p>Check for world-writable files (security risk) find / -type f -perm -0002 ! -path "/proc/" ! -path "/sys/" 2>/dev/null</p></li> <li><p>Verify that auditd is running sudo systemctl status auditd
What Undercode Say:
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Key Takeaway 1: The offense-defense gap is widening, and non-technical organizations are the most vulnerable. The National Academies report makes it clear that attackers are already benefiting from AI capabilities, and the risks for organizations without in-house security expertise are accelerating faster than the ability to manage them. This is not a problem that can be outsourced entirely to vendors — organizations must build their own risk awareness and response capabilities.
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Key Takeaway 2: Measurement is the missing link. Without systematic benchmarks and metrics, we cannot track changes in AI-related cyber risk or attacker-defender advantage. The emergence of frameworks like CAIBench, CyberSOCEval, and PACEbench is encouraging, but these need to be adopted widely and integrated into organizational security programs. The gap between AI knowledge and AI capability — 70% knowledge success versus 20-40% multi-step adversarial success — reveals that we are only beginning to understand what AI can and cannot do in cybersecurity contexts.
Analysis: The National Academies’ rapid expert consultation arrives at a pivotal moment. The report’s conclusion that “AI may widen the near-term gap between attackers and defenders” should serve as a wake-up call for every organization, regardless of technical maturity. The short-term outlook is concerning precisely because the mechanisms for measuring and responding to AI-enhanced threats are underdeveloped. However, the long-term potential is real: AI could enable a transition from reactive, episodic security to continuous, adaptive defense-in-depth.
The challenge is one of time compression. Attackers are adopting AI faster than defenders can respond, and the gap is most pronounced for organizations that lack in-house security expertise. The report’s emphasis on investment across technical, architectural, and institutional dimensions underscores that technology alone is insufficient. Incentives must be aligned, policies must be enforced, and benchmarks must be established.
Alfonso De Gregorio’s work on systemic assessment of open-weight models represents exactly the kind of foundational research needed to fill the measurement gap. As open-weight models proliferate, the ability to assess their security implications becomes not just an academic exercise but a practical necessity for every organization that deploys or depends on AI systems.
Prediction:
- +1 The emergence of comprehensive AI cybersecurity benchmarks (CAIBench, CyberSOCEval, PACEbench) will, within 12-18 months, enable organizations to quantitatively measure their AI security posture, driving a new wave of informed investment in defensive AI capabilities.
- -1 The offense-defense gap will widen further in the next 6-12 months as threat actors increasingly deploy agentic AI systems capable of autonomous reconnaissance, exploitation, and lateral movement, reducing attack timelines from days to minutes.
- +1 Open-weight model security assessments will mature into standardized frameworks, enabling organizations to safely leverage these models while implementing layered security controls that mitigate the unique risks of open-weight deployments.
- -1 Non-technical organizations will experience a disproportionate share of AI-powered breaches, as their reliance on vendor-managed security and slower patch cycles leaves them exposed to AI-accelerated vulnerability discovery and exploitation.
- +1 The transition to continuous AI-enhanced defense-in-depth will begin in earnest by 2027, with early adopters demonstrating that AI can shift the advantage back to defenders through automated threat hunting, real-time vulnerability remediation, and intelligent threat intelligence sharing.
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