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Official guidance does not show that AI has made attacking software measurably cheaper. None of the official publications discussed here measures what an attack costs, and none attributes a rise in incidents to AI alone. What they do support is narrower and more useful. AI is dual-use, it can help attackers automate and scale their activity, and defenders can raise the price of attacking their software by removing common flaws earlier, shrinking exposure, ranking vulnerabilities by real-world risk, and confirming that fixes actually work.
What the evidence does and does not establish
The headline claim is a thesis worth testing, not a finding. Before choosing defenses, it helps to separate what the sources say from what would still need evidence.
| Claim | What the official sources say | Status |
|---|---|---|
| AI is dual-use | NIST says AI can give defenders new tools to address vulnerabilities and can also enhance the capabilities of people targeting organizations and individuals (NIST, “AI Research – Security and Resilience,” updated August 14, 2026). | Established as a capability claim |
| AI can help threat actors automate and scale | CISA’s summary of its FY2024–FY2025 Vulnerability Review (published August 26, 2026) says emerging technology, including AI, introduces efficiencies that threat actors can use to automate and scale activity. | Established as a capability claim; no measured effect given |
| AI has made attacking software cheaper | None of the cited sources measures attacker cost, time, or effort before and after AI tools. | Not established; this is the thesis to test |
| AI has made attacks more successful | No source reviewed reports success rates for AI-assisted attacks. | Not established |
| AI alone caused a rise in incidents | No cited source makes this attribution. | Not established |
The gap matters. A capability is a reason to expect change; a measured cost reduction is what would justify the word “cheaper.” The official sources provide the first but not the second.
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Why AI cuts both ways
NIST’s position is that AI is dual-use. The same capabilities that let an attacker probe a system faster can let a defender find and fix weaknesses faster. That is why the useful question is not whether AI helps attackers, but where it changes the balance of effort.
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The mechanism most often cited is labor and iteration. AI-assisted coding, vulnerability discovery, and task automation could reduce the time an attacker spends on each step of a campaign. That is a plausible mechanism, not a quantified trend. The official sources reviewed do not measure how much time or money it saves, so it should be treated as a hypothesis that needs evidence before it is stated as established.
The same logic applies on defense. Scanning, triage, and patch prioritization are tasks that automation can also speed up. Whether the net effect favors one side depends on which side adopts and operationalizes the tools faster, and the sources reviewed do not answer that question.
Simple weaknesses still do most of the damage
CISA’s summary of the FY2024–FY2025 Vulnerability Review says many threat actors scan for and exploit simple, known vulnerabilities. The review highlights four recurring problem areas:
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- Memory safety vulnerabilities, which remain a common class of weakness in software.
- Poor patching, meaning known flaws are left unfixed after fixes exist.
- End-of-support technology, which no longer receives fixes at all.
This is the most practical point for the economics argument. If a large share of successful attacks rely on flaws that are already known or easy to find, then removing those flaws from software and from the environment raises the attacker’s cost more directly than any single new control.
How to change the economics: four levers
A defender cannot change what a determined attacker is willing to spend. It can change the payoff per attempt and the friction an attacker meets. The four levers below map onto that idea. Each is grounded in the cited guidance, and the framing of “payoff” and “friction” is analysis rather than a CISA metric.
1. Remove common flaws before software ships
NIST Special Publication 800-218A, finalized on July 26, 2024, adds AI-specific secure development practices and tasks to the Secure Software Development Framework (SSDF) 1.1. It is written for AI model producers, AI system producers, and acquirers of AI. Its value for the price argument is that it moves prevention upstream, where the weakness classes CISA highlights, such as improper input validation and memory safety, can be addressed before deployment.
2. Shrink what attackers can reach
Every internet-exposed service and every end-of-support system is a standing offer to an attacker. Reducing that inventory lowers the number of targets that automated scanning can find cheaply. A practical sequence looks like this:
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- Build a complete list of internet-facing services, including those run by teams outside central IT.
- Flag every system running technology that is past end of support.
- Scan the external footprint. CISA’s bulletin points to its no-cost Cyber Hygiene scanning service.
- Remove or restrict services that are not needed, following CISA’s Internet Exposure Reduction Guidance.
3. Rank vulnerabilities by risk, not by count
CISA lists four criteria for prioritization: exposure, whether a vulnerability is on the Known Exploited Vulnerabilities (KEV) list, the potential for automated exploitation, and technical impact. A flaw that is internet-reachable, already exploited in the wild, easy to automate against, and severe in impact should move to the front of the queue. A high-severity flaw on an isolated internal system may not.
CISA also points to its Stakeholder-Specific Vulnerability Categorization material and to Vulnrichment, which provides machine-readable signals that can feed prioritization tools. Teams that already run vulnerability scanners can use those signals to sort their backlog without reviewing each entry by hand.
4. Confirm that the fix actually closed the path
A ticket marked “patched” does not change an attacker’s economics until the fix is live on every affected asset. Verification closes that gap. Checks worth running include confirming the update on each affected host, rescanning the external footprint to confirm the exposed service is no longer reachable, and making sure end-of-support systems have not returned through a replacement or a forgotten instance.
Securing the AI systems you deploy
Most organizations will use AI they did not build. Joint guidance titled Joint Guidance on Deploying AI Systems Securely, announced on April 15, 2024, addresses that situation. It covers operating externally developed AI systems and focuses on confidentiality, integrity, availability, mitigation of known vulnerabilities, and protection, detection, and response controls.
The two NIST and CISA documents serve different stages of the same system’s life, as the table below shows.
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| Document | Date | Audience | Main focus |
|---|---|---|---|
| NIST SP 800-218A (extends SSDF 1.1) | Final July 26, 2024 | AI model producers, AI system producers, acquirers | Secure development practices and tasks for AI, applied during building |
| Joint Guidance on Deploying AI Systems Securely (CISA and partners) | Announced April 15, 2024 | Organizations operating externally developed AI systems | Confidentiality, integrity, availability, known-vulnerability mitigation, and protect, detect, and respond controls during deployment |
In practice, a team that adopts a third-party AI tool should apply the deployment controls to what it runs and the secure development guidance to anything it builds on top of it.
Independent reader supportYour contribution helps us test, update, and keep practical guides available for everyone.Measuring whether the change works
CISA’s Cybersecurity Strategic Plan, titled Shifting the Arc of National Risk to Create a Safer Future, names two measures of effectiveness: time to detect adversary activity and time to fix Known Exploited Vulnerabilities. Published summaries of the plan do not give a numeric target for either measure, so any target a team sets is its own choice.
Speed only matters when it reduces real exposure. Closing a KEV on an isolated system in a day does less for the price of attack than closing one on an internet-facing system in a week. Teams can make the measure meaningful by splitting results by exposure, so that time-to-fix is reported separately for internet-facing and internal assets.
Comparing defensive options
When choosing between investments, four questions separate options that look similar on paper.
| Axis | Question to ask | Example |
|---|---|---|
| Prevention or response | Does this stop the flaw from existing, or detect and respond after deployment? | Secure development practices versus protect, detect, and respond controls |
| Exposure and exploitability | Is the weakness reachable from the internet, on the KEV list, or easy to automate against? | An internet-facing KEV ranked above an isolated high-severity flaw |
| Time to effective remediation | How long until the fix is confirmed live on every affected asset? | Patch deployed and verified by rescanning, not just approved |
| Audience | Is the work for building AI systems or for deploying externally developed ones? | NIST SP 800-218A versus the joint deployment guidance |
Limits of this assessment
- The cost claim is untested. Showing that attacks have become cheaper would require measured data on attacker time, tools, or spending before and after AI adoption. The sources cited here do not provide that data.
- The guidance is still developing. NIST describes its AI-security work as active research and says existing frameworks do not comprehensively address several AI-specific attacks and attack surfaces.
- The CISA review is a published summary. Detailed counts and tables sit in the full review, which should be consulted before reproducing any underlying figures. This article does not use such counts.
- Patching is one lever, not the whole defense. The guidance pairs prioritization and remediation with secure-by-design practices and controls that limit what an attacker can reach after deployment.
The honest version of the headline is therefore a conditional one. AI can lower some of the effort attackers spend, and defenders can offset that by making known weaknesses scarcer, exposure smaller, and verified fixes faster.
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